Interrogatory cell-based assays and uses thereof

ABSTRACT

Described herein is a discovery Platform Technology for analyzing a biological system or process (e.g., a disease condition, such as cancer) via model building.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 61/448,587, filed Mar. 2, 2011, and U.S. Provisional ApplicationSer. No. 61/593,848, filed Feb. 2, 2012, the entire contents of each ofwhich are incorporated herein.

BACKGROUND OF THE INVENTION

New drug development has been enhanced greatly since the discovery ofthe chemical structure of DNA in 1953 by James Watson and Francis Crick,pioneers of what we refer today as molecular biology. The tools andproducts of molecular biology allow for rapid, detailed, and precisemeasurement of gene regulation at both the DNA and RNA level. The nextthree decades following the paradigm-shifting discovery would see thegenesis of knock-out animal models, key enzyme-linked reactions, andnovel understanding of disease mechanisms and pathophysiology from theaforementioned platforms. In spring 2000, when Craig Venter and FrancisCollins announced the initial sequencing of the human genome, thescientific world entered a new wave of medicine.

The mapping of the genome immediately sparked hopes of, for example,being able to control disease even before it was initiated, of usinggene therapy to reverse the degenerative brain processes that causesAlzheimer's or Parkinson's Disease, and of a construct that could beintroduced to a tumor site and cause eradication of disease whilerestoring the normal tissue architecture and physiology. Others tookcontroversial twists and proposed the notion of creating desiredoffspring with respect to eye or hair color, height, etc. Ten yearslater, however, we are still waiting with no particular path in sightfor sustained success of gene therapy, or even elementary control of thegenetic process.

Thus, one apparent reality is that genetics, at least independent ofsupporting constructs, does not drive the end-point of physiology.Indeed, many processes such as post-transcriptional modifications,mutations, single-nucleotide polymorphisms (SNP's), and translationalmodifications could alter the providence of a gene and/or its encodedcomplementary protein, and thereby contribute to the disease process.

SUMMARY OF THE INVENTION

The information age and creation of the internet has allowed for aninformation overload, while also facilitating internationalcollaboration and critique. Ironically, the aforementioned realities mayalso be the cause of the scientific community overlooking a few simplepoints, including that communication of signal cascades and cross-talkwithin and between cells and/or tissues allows for homeostasis andmessaging for corrective mechanisms to occur when something goes awry.

A case on point relates to cardiovascular disease (CVD), which remainsthe leading cause of death in the United States and much of thedeveloped world, accounting for 1 of every 2.8 deaths in the U.S. alone.In addition, CVD serves as an underlying pathology that contributes toassociated complications such as Chronic Kidney Disease (˜19 million UScases), chronic fatigue syndrome, and a key factor in metabolicsyndrome. Significant advances in technology related to diagnostics,minimally invasive surgical techniques, drug eluting stents andeffective clinical surveillance has contributed to an unparalleledperiod of growth in the field of interventional cardiology, and hasallowed for more effective management of CVD. However, disease etiologyrelated to CVD and associated co-morbidities such as diabetes andperipheral vascular disease are yet to be fully elucidated.

New approaches to explore the mechanisms and pathways involved in abiological process, such as the etiology of disease conditions (e.g.,CVD), and to identify key regulatory pathways and/or target molecules(e.g., “drugable targets”) and/or markers for better disease diagnosis,management, and/or treatment, are still lacking.

The invention described herein is based, at least in part, on a novel,collaborative utilization of network biology, genomic, proteomic,metabolomic, transcriptomic, and bioinformatics tools and methodologies,which, when combined, may be used to study any biological system ofinterest, such as selected disease conditions including cancer,diabetes, obesity and cardiovascular disease, using a systems biologyapproach. In a first step, cellular modeling systems are developed toprobe various biological systems, such as a disease process, comprisingdisease-related cells subjected to various disease-relevant environmentstimuli (e.g., hyperglycemia, hypoxia, immuno-stress, and lipidperoxidation). In some embodiments, the cellular modeling systeminvolves cellular cross-talk mechanisms between various interacting celltypes (such as aortic smooth muscle cells (HASMC), proximal tubulekidney cells (HK-2), aortic endothelial cells (HAEC), and dermalfibroblasts (HDFa)). High throughput biological readouts from the cellmodel system are obtained by using a combination of techniques,including, for example, cutting edge mass spectrometry (LC/MSMS), flowcytometry, cell-based assays, and functional assays. The high throughputbiological readouts are then subjected to a bioinformatic analysis tostudy congruent data trends by in vitro, in vivo, and in silicomodeling. The resulting matrices allow for cross-related data miningwhere linear and non-linear regression analysis were developed to reachconclusive pressure points (or “hubs”). These “hubs”, as presentedherein, are candidates for drug discovery. In particular, these hubsrepresent potential drug targets and/or disease markers.

The molecular signatures of the differentials allow for insight into themechanisms that dictate the alterations in the tissue microenvironmentthat lead to disease onset and progression. Taken together, thecombination of the aforementioned technology platforms with strategiccellular modeling allows for robust intelligence that can be employed tofurther establish disease understanding while creating biomarkerlibraries and drug candidates that may clinically augment standard ofcare in interventional cardiology.

Moreover, this approach is not only useful for disease diagnosis orintervention, but also has general applicability to virtually allpathological or non-pathological conditions in biological systems, suchas biological systems where two or more cell systems interact. Forexample, this approach is useful for obtaining insight into themechanisms associated with or causal for drug toxicity. The inventiontherefore provides a framework for an interrogative biologicalassessment that can be generally applied in a broad spectrum ofsettings.

A significant feature of the platform of the invention is that theAI-based system is based on the data sets obtained from the cell modelsystem, without resorting to or taking into consideration any existingknowledge in the art, such as known biological relationships (i.e., nodata points are artificial), concerning the biological process.Accordingly, the resulting statistical models generated from theplatform are unbiased. Another significant feature of the platform ofthe invention and its components, e.g., the cell model systems and datasets obtained therefrom, is that it allows for continual building on thecell models over time (e.g., by the introduction of new cells and/orconditions), such that an initial, “first generation” consensus causalrelationship network generated from a cell model for a biological systemor process can evolve along with the evolution of the cell model itselfto a multiple generation causal relationship network (and delta ordelta-delta networks obtained therefrom). In this way, both the cellmodels, the data sets from the cell models, and the causal relationshipnetworks generated from the cell models by using the Platform Technologymethods can constantly evolve and build upon previous knowledge obtainedfrom the Platform Technology.

Accordingly, in one aspect, the invention relates to a method foridentifying a modulator of a biological system, said method comprising:(1) establishing a model for the biological system, using cellsassociated with the biological system, to represents a characteristicaspect of the biological system; (2) obtaining a first data set from themodel for the biological system, wherein the first data set representsexpression levels of a plurality of genes in the cells associated withthe biological system; (3) obtaining a second data set from the modelfor the biological system, wherein the second data set represents afunctional activity or a cellular response of the cells associated withthe biological system; (4) generating a consensus causal relationshipnetwork among the expression levels of the plurality of genes and thefunctional activity or cellular response based solely on the first dataset and the second data set using a programmed computing device, whereinthe generation of the consensus causal relationship network is not basedon any known biological relationships other than the first data set andthe second data set; (5) identifying, from the consensus causalrelationship network, a causal relationship unique in the biologicalsystem, wherein a gene associated with the unique causal relationship isidentified as a modulator of the biological system.

In certain embodiments, the modulator stimulates or promotes thebiological system.

In certain embodiments, the modulator inhibits the biological system.

In certain embodiments, the model of the biological system comprises anin vitro culture of cells associated with the biological system,optionally further comprising a matching in vitro culture of controlcells.

In certain embodiments, the in vitro culture of the cells is subject toan environmental perturbation, and the in vitro culture of the matchingcontrol cells is identical cells not subject to the environmentalperturbation.

In certain embodiments, the environmental perturbation comprises one ormore of a contact with an agent, a change in culture condition, anintroduced genetic modification/mutation, and a vehicle (e.g., vector)that causes a genetic modification/mutation.

In certain embodiments, the first data set comprises protein and/or mRNAexpression levels of the plurality of genes.

In certain embodiments, the first data set further comprises one or moreof lipidomics data, metabolomics data, transcriptomics data, and singlenucleotide polymorphism (SNP) data.

In certain embodiments, the second data set comprises one or more ofbioenergetics profiling, cell proliferation, apoptosis, organellarfunction, and a genotype-phenotype association actualized by functionalmodels selected from ATP, ROS, OXPHOS, and Seahorse assays.

In certain embodiments, step (4) is carried out by an artificialintelligence (AI)-based informatics platform.

In certain embodiments, the AI-based informatics platform comprisesREFS™.

In certain embodiments, the AI-based informatics platform receives alldata input from the first data set and the second data set withoutapplying a statistical cut-off point.

In certain embodiments, the consensus causal relationship networkestablished in step (4) is further refined to a simulation causalrelationship network, before step (5), by in silico simulation based oninput data, to provide a confidence level of prediction for one or morecausal relationships within the consensus causal relationship network.

In certain embodiments, the unique causal relationship is identified aspart of a differential causal relationship network that is uniquelypresent in cells, and absent in the matching control cells.

In certain embodiments, the method further comprising validating theidentified unique causal relationship in a biological system.

In another aspect, the invention relates to a method for identifying amodulator of a disease process, said method comprising: (1) establishinga disease model for the disease process, using disease related cells, torepresents a characteristic aspect of the disease process; (2) obtaininga first data set from the disease model, wherein the first data setrepresents expression levels of a plurality of genes in the diseaserelated cells; (3) obtaining a second data set from the disease model,wherein the second data set represents a functional activity or acellular response of the disease related cells; (4) generating aconsensus causal relationship network among the expression levels of theplurality of genes and the functional activity or cellular responsebased solely on the first data set and the second data set using aprogrammed computing device, wherein the generation of the consensuscausal relationship network is not based on any known biologicalrelationships other than the first data set and the second data set; (5)identifying, from the consensus causal relationship network, a causalrelationship unique in the disease process, wherein a gene associatedwith the unique causal relationship is identified as a modulator of adisease process.

In certain embodiments, the disease process is cancer, diabetes, obesityor cardiovascular disease.

In certain embodiments, the cancer is lung cancer, breast cancer,prostate cancer, melanoma, squamous cell carcinoma, colorectal cancer,pancreatic cancer, thyroid cancer, endometrial cancer, bladder cancer,kidney cancer, a solid tumor, leukemia, non-Hodgkin lymphoma, or adrug-resistant cancer.

In certain embodiments, the modulator stimulates or promotes the diseaseprocess.

In certain embodiments, the modulator inhibits the disease process.

In certain embodiments, the modulator shifts the energy metabolicpathway specifically in disease cells from a glycolytic pathway towardsan oxidative phosphorylation pathway.

In certain embodiments, the disease model comprises an in vitro cultureof disease cells, optionally further comprising a matching in vitroculture of control or normal cells.

In certain embodiments, the in vitro culture of the disease cells issubject to an environmental perturbation, and the in vitro culture ofthe matching control cells is identical disease cells not subject to theenvironmental perturbation.

In certain embodiments, the environmental perturbation comprises one ormore of a contact with an agent, a change in culture condition, anintroduced genetic modification/mutation, and a vehicle (e.g., vector)that causes a genetic modification/mutation.

In certain embodiments, the characteristic aspect of the disease processcomprises a hypoxia condition, a hyperglycemic condition, a lactic acidrich culture condition, or combinations thereof.

In certain embodiments, the first data set comprises protein and/or mRNAexpression levels of the plurality of genes.

In certain embodiments, the first data set further comprises one or moreof lipidomics data, metabolomics data, transcriptomics data, and singlenucleotide polymorphism (SNP) data.

In certain embodiments, the second data set comprises one or more ofbioenergetics profiling, cell proliferation, apoptosis, organellarfunction, and a genotype-phenotype association actualized by functionalmodels selected from ATP, ROS, OXPHOS, and Seahorse assays.

In certain embodiments, step (4) is carried out by an artificialintelligence (AI)-based informatics platform.

In certain embodiments, the AI-based informatics platform comprisesREFS™.

In certain embodiments, the AI-based informatics platform receives alldata input from the first data set and the second data set withoutapplying a statistical cut-off point.

In certain embodiments, the consensus causal relationship networkestablished in step (4) is further refined to a simulation causalrelationship network, before step (5), by in silico simulation based oninput data, to provide a confidence level of prediction for one or morecausal relationships within the consensus causal relationship network.

In certain embodiments, the unique causal relationship is identified aspart of a differential causal relationship network that is uniquelypresent in disease cells, and absent in the matching control cells.

In certain embodiments, the method further comprising validating theidentified unique causal relationship in a biological system.

In another aspect, the invention relates to a method for providing amodel for a biological system for use in a platform method, comprising:establishing a model for a biological system, using cells associatedwith the biological system, to represent a characteristic aspect of thebiological system, wherein the model for the biological system is usefulfor generating data sets used in the platform method; thereby providinga model for a biological system for use in a platform method.

In another aspect, the invention relates to a method for obtaining afirst data set and second data set from a model for a biological systemfor use in a platform method, comprising: (1) obtaining a first data setfrom the model for a biological system for use in a platform method,wherein the model for the biological system comprises cells associatedwith the biological system, and wherein the first data set representsexpression levels of a plurality of genes in the cells associated withthe biological system; (2) obtaining a second data set from the modelfor a biological system for use in the platform method, wherein thesecond data set represents a functional activity or a cellular responseof the cells associated with the biological system; thereby obtaining afirst data set and second data set from the model for the biologicalsystem for use in a platform method.

In another aspect, the invention relates to a method for identifying amodulator of a biological system, said method comprising: (1) generatinga consensus causal relationship network among a first data set andsecond data set obtained from a model for the biological system, whereinthe model comprises cells associated with the biological system, andwherein the first data set represents expression levels of a pluralityof genes in the cells and the second data set represents a functionalactivity or a cellular response of the cells, using a programmedcomputing device, wherein the generation of the consensus causalrelationship network is not based on any known biological relationshipsother than the first data set and the second data set; (2) identifying,from the consensus causal relationship network, a causal relationshipunique in the biological system, wherein a gene associated with theunique causal relationship is identified as a modulator of thebiological system; thereby identifying a modulator of a biologicalsystem.

In another aspect, the invention relates to a method for identifying amodulator of a biological system, said method comprising: 1) providing aconsensus causal relationship network generated from a model for thebiological system; 2) identifying, from the consensus causalrelationship network, a causal relationship unique in the biologicalsystem, wherein a gene associated with the unique causal relationship isidentified as a modulator of the biological system; thereby identifyinga modulator of a biological system.

In certain embodiments of the various methods, the consensus causalrelationship network is generated among a first data set and second dataset obtained from the model for the biological system, wherein the modelcomprises cells associated with the biological system, and wherein thefirst data set represents expression levels of a plurality of genes inthe cells and the second data set represents a functional activity or acellular response of the cells, using a programmed computing device,wherein the generation of the consensus causal relationship network isnot based on any known biological relationships other than the firstdata set and the second data set.

In another aspect, the invention relates to a method for providing adisease model for use in a platform method, comprising: establishing adisease model for a disease process, using disease related cells, torepresent a characteristic aspect of the disease process, wherein thedisease model is useful for generating disease model data sets used inthe platform method; thereby providing a disease model for use in aplatform method.

In another aspect, the invention relates to a method for obtaining afirst data set and second data set from a disease model for use in aplatform method, comprising: (1) obtaining a first data set from adisease model for use in a platform method, wherein the disease modelcomprises disease related cells, and wherein the first data setrepresents expression levels of a plurality of genes in the diseaserelated cells; (2) obtaining a second data set from the disease modelfor use in a platform method, wherein the second data set represents afunctional activity or a cellular response of the disease related cells;thereby obtaining a first data set and second data set from the diseasemodel; thereby obtaining a first data set and second data set from adisease model for use in a platform method.

In another aspect, the invention relates to a method for identifying amodulator of a disease process, said method comprising: (1) generating aconsensus causal relationship network among a first data set and seconddata set obtained from a disease model, wherein the disease modelcomprises disease cells, and wherein the first data set representsexpression levels of a plurality of genes in the disease related cellsand the second data set represents a functional activity or a cellularresponse of the disease related cells, using a programmed computingdevice, wherein the generation of the consensus causal relationshipnetwork is not based on any known biological relationships other thanthe first data set and the second data set; (2) identifying, from theconsensus causal relationship network, a causal relationship unique inthe disease process, wherein a gene associated with the unique causalrelationship is identified as a modulator of a disease process; therebyidentifying a modulator of a disease process.

In another aspect, the invention relates to a method for identifying amodulator of a disease process, said method comprising: 1) providing aconsensus causal relationship network generated from a disease model forthe disease process; 2) identifying, from the consensus causalrelationship network, a causal relationship unique in the diseaseprocess, wherein a gene associated with the unique causal relationshipis identified as a modulator of a disease process; thereby identifying amodulator of a disease process.

In certain embodiments, the consensus causal relationship network isgenerated among a first data set and second data set obtained from thedisease model for the disease process, wherein the disease modelcomprises disease cells, and wherein the first data set representsexpression levels of a plurality of genes in the disease related cellsand the second data set represents a functional activity or a cellularresponse of the disease related cells, using a programmed computingdevice, wherein the generation of the consensus causal relationshipnetwork is not based on any known biological relationships other thanthe first data set and the second data set.

In certain embodiments, the “environmental perturbation”, also referredto herein as “external stimulus component”, is a therapeutic agent. Incertain embodiments, the external stimulus component is a small molecule(e.g., a small molecule of no more than 5 kDa, 4 kDa, 3 kDa, 2 kDa, 1kDa, 500 Dalton, or 250 Dalton). In certain embodiments, the externalstimulus component is a biologic. In certain embodiments, the externalstimulus component is a chemical. In certain embodiments, the externalstimulus component is endogenous or exogenous to cells. In certainembodiments, the external stimulus component is a MIM or epishifter. Incertain embodiments, the external stimulus component is a stress factorfor the cell system, such as hypoxia, hyperglycemia, hyperlipidemia,hyperinsulinemia, and/or lactic acid rich conditions.

In certain embodiments, the external stimulus component may include atherapeutic agent or a candidate therapeutic agent for treating adisease condition, including chemotherapeutic agent, protein-basedbiological drugs, antibodies, fusion proteins, small molecule drugs,lipids, polysaccharides, nucleic acids, etc.

In certain embodiments, the external stimulus component may be one ormore stress factors, such as those typically encountered in vivo underthe various disease conditions, including hypoxia, hyperglycemicconditions, acidic environment (that may be mimicked by lactic acidtreatment), etc.

In other embodiments, the external stimulus component may include one ormore MIMs and/or epishifters, as defined herein below. Exemplary MIMsinclude Coenzyme Q10 (also referred to herein as CoQ10) and compounds inthe Vitamin B family, or nucleosides, mononucleotides or dinucleotidesthat comprise a compound in the Vitamin B family.

In making cellular output measurements (such as protein expression),either absolute amount (e.g., expression amount) or relative level(e.g., relative expression level) may be used. In one embodiment,absolute amounts (e.g., expression amounts) are used. In one embodiment,relative levels or amounts (e.g., relative expression levels) are used.For example, to determine the relative protein expression level of acell system, the amount of any given protein in the cell system, with orwithout the external stimulus to the cell system, may be compared to asuitable control cell line or mixture of cell lines (such as all cellsused in the same experiment) and given a fold-increase or fold-decreasevalue. The skilled person will appreciate that absolute amounts orrelative amounts can be employed in any cellular output measurement,such as gene and/or RNA transcription level, level of lipid, or anyfunctional output, e.g., level of apoptosis, level of toxicity, or ECARor OCR as described herein. A pre-determined threshold level for afold-increase (e.g., at least 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2,2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50,75 or 100 or more fold increase) or fold-decrease (e.g., at least adecrease to 0.9, 0.8, 0.75, 0.7, 0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25,0.2, 0.15, 0.1 or 0.05 fold, or a decrease to 90%, 80%, 75%, 70%, 65%,60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10% or 5% or less) maybe used to select significant differentials, and the cellular outputdata for the significant differentials may then be included in the datasets (e.g., first and second data sets) utilized in the platformtechnology methods of the invention. All values presented in theforegoing list can also be the upper or lower limit of ranges, e.g.,between 1.5 and 5 fold, 5 and 10 fold, 2 and 5 fold, or between 0.9 and0.7, 0.9 and 0.5, or 0.7 and 0.3 fold, are intended to be a part of thisinvention.

Throughout the present application, all values presented in a list,e.g., such as those above, can also be the upper or lower limit ofranges that are intended to be a part of this invention.

In one embodiment of the methods of the invention, not every observedcausal relationship in a causal relationship network may be ofbiological significance. With respect to any given biological system forwhich the subject interrogative biological assessment is applied, some(or maybe all) of the causal relationships (and the genes associatedtherewith) may be “determinative” with respect to the specificbiological problem at issue, e.g., either responsible for causing adisease condition (a potential target for therapeutic intervention) oris a biomarker for the disease condition (a potential diagnostic orprognostic factor). In one embodiment, an observed causal relationshipunique in the biological system is determinative with respect to thespecific biological problem at issue. In one embodiment, not everyobserved causal relationship unique in the biological system isdeterminative with respect to the specific problem at issue.

Such determinative causal relationships may be selected by an end userof the subject method, or it may be selected by a bioinformaticssoftware program, such as REFS, DAVID-enabled comparative pathwayanalysis program, or the KEGG pathway analysis program. In certainembodiments, more than one bioinformatics software program is used, andconsensus results from two or more bioinformatics software programs arepreferred.

As used herein, “differentials” of cellular outputs include differences(e.g., increased or decreased levels) in any one or more parameters ofthe cellular outputs. In certain embodiments, the differentials are eachindependently selected from the group consisting of differentials inmRNA transcription, protein expression, protein activity,metabolite/intermediate level, and/or ligand-target interaction. Forexample, in terms of protein expression level, differentials between twocellular outputs, such as the outputs associated with a cell systembefore and after the treatment by an external stimulus component, can bemeasured and quantitated by using art-recognized technologies, such asmass-spectrometry based assays (e.g., iTRAQ, 2D-LC-MSMS, etc.).

In one aspect, the cell model for a biological system comprises acellular cross-talking system, wherein a first cell system having afirst cellular environment with an external stimulus component generatesa first modified cellular environment; such that a cross-talking cellsystem is established by exposing a second cell system having a secondcellular environment to the first modified cellular environment.

In one embodiment, at least one significant cellular cross-talkingdifferential from the cross-talking cell system is generated; and atleast one determinative cellular cross-talking differential isidentified such that an interrogative biological assessment occurs. Incertain embodiments, the at least one significant cellular cross-talkingdifferential is a plurality of differentials.

In certain embodiments, the at least one determinative cellularcross-talking differential is selected by the end user. Alternatively,in another embodiment, the at least one determinative cellularcross-talking differential is selected by a bioinformatics softwareprogram (such as, e.g., REFS, KEGG pathway analysis or DAVID-enabledcomparative pathway analysis) based on the quantitative proteomics data.

In certain embodiments, the method further comprises generating asignificant cellular output differential for the first cell system.

In certain embodiments, the differentials are each independentlyselected from the group consisting of differentials in mRNAtranscription, protein expression, protein activity,metabolite/intermediate level, and/or ligand-target interaction.

In certain embodiments, the first cell system and the second cell systemare independently selected from: a homogeneous population of primarycells, a cancer cell line, or a normal cell line.

In certain embodiments, the first modified cellular environmentcomprises factors secreted by the first cell system into the firstcellular environment, as a result of contacting the first cell systemwith the external stimulus component. The factors may comprise secretedproteins or other signaling molecules. In certain embodiments, the firstmodified cellular environment is substantially free of the originalexternal stimulus component.

In certain embodiments, the cross-talking cell system comprises atranswell having an insert compartment and a well compartment separatedby a membrane. For example, the first cell system may grow in the insertcompartment (or the well compartment), and the second cell system maygrow in the well compartment (or the insert compartment).

In certain embodiments, the cross-talking cell system comprises a firstculture for growing the first cell system, and a second culture forgrowing the second cell system. In this case, the first modifiedcellular environment may be a conditioned medium from the first cellsystem.

In certain embodiments, the first cellular environment and the secondcellular environment can be identical. In certain embodiments, the firstcellular environment and the second cellular environment can bedifferent.

In certain embodiments, the cross-talking cell system comprises aco-culture of the first cell system and the second cell system.

The methods of the invention may be used for, or applied to, any numberof “interrogative biological assessments.” Application of the methods ofthe invention to an interrogative biological assessment allows for theidentification of one or more modulators of a biological system ordeterminative cellular process “drivers” of a biological system orprocess.

The methods of the invention may be used to carry out a broad range ofinterrogative biological assessments. In certain embodiments, theinterrogative biological assessment is the diagnosis of a disease state.In certain embodiments, the interrogative biological assessment is thedetermination of the efficacy of a drug. In certain embodiments, theinterrogative biological assessment is the determination of the toxicityof a drug. In certain embodiments, the interrogative biologicalassessment is the staging of a disease state. In certain embodiments,the interrogative biological assessment identifies targets foranti-aging cosmetics.

As used herein, an “interrogative biological assessment” may include theidentification of one or more modulators of a biological system, e.g.,determinative cellular process “drivers,” (e.g., an increase or decreasein activity of a biological pathway, or key members of the pathway, orkey regulators to members of the pathway) associated with theenvironmental perturbation or external stimulus component, or a uniquecausal relationship unique in a biological system or process. It mayfurther include additional steps designed to test or verify whether theidentified determinative cellular process drivers are necessary and/orsufficient for the downstream events associated with the environmentalperturbation or external stimulus component, including in vivo animalmodels and/or in vitro tissue culture experiments.

In certain embodiments, the interrogative biological assessment is thediagnosis or staging of a disease state, wherein the identifiedmodulators of a biological system, e.g., determinative cellular processdrivers (e.g., cross-talk differentials or causal relationships uniquein a biological system or process) represent either disease markers ortherapeutic targets that can be subject to therapeutic intervention. Thesubject interrogative biological assessment is suitable for any diseasecondition in theory, but may found particularly useful in areas such asoncology/cancer biology, diabetes, obesity, cardiovascular disease, andneurological conditions (especially neuro-degenerative diseases, suchas, without limitation, Alzheimer's disease, Parkinson's disease,Huntington's disease, Amyotrophic lateral sclerosis (ALS), and agingrelated neurodegeneration).

In certain embodiments, the interrogative biological assessment is thedetermination of the efficacy of a drug, wherein the identifiedmodulators of a biological system, e.g., determinative cellular processdriver (e.g., cross-talk differentials or causal relationships unique ina biological system or process) may be the hallmarks of a successfuldrug, and may in turn be used to identify additional agents, such asMIMs or epishifters, for treating the same disease condition.

In certain embodiments, the interrogative biological assessment is theidentification of drug targets for preventing or treating infection,wherein the identified determinative cellular process driver (e.g.,cellular cross-talk differentials or causal relationships unique in abiological system or process) may be markers/indicators or keybiological molecules causative of the infective state, and may in turnbe used to identify anti-infective agents.

In certain embodiments, the interrogative biological assessment is theassessment of a molecular effect of an agent, e.g., a drug, on a givendisease profile, wherein the identified modulators of a biologicalsystem, e.g., determinative cellular process driver (e.g., cellularcross-talk differentials or causal relationships unique in a biologicalsystem or process) may be an increase or decrease in activity of one ormore biological pathways, or key members of the pathway(s), or keyregulators to members of the pathway(s), and may in turn be used, e.g.,to predict the therapeutic efficacy of the agent for the given disease.

In certain embodiments, the interrogative biological assessment is theassessment of the toxicological profile of an agent, e.g., a drug, on acell, tissue, organ or organism, wherein the identified modulators of abiological system, e.g., determinative cellular process driver (e.g.,cellular cross-talk differentials or causal relationships unique in abiological system or process) may be indicators of toxicity, e.g.,cytotoxicity, and may in turn be used to predict or identify thetoxicological profile of the agent. In one embodiment, the identifiedmodulators of a biological system, e.g., determinative cellular processdriver (e.g., cellular cross-talk differentials or causal relationshipsunique in a biological system or process) is an indicator ofcardiotoxicity of a drug or drug candidate, and may in turn be used topredict or identify the cardiotoxicological profile of the drug or drugcandidate.

In certain embodiments, the interrogative biological assessment is theidentification of drug targets for preventing or treating a disease ordisorder caused by biological weapons, such as disease-causing protozoa,fungi, bacteria, protests, viruses, or toxins, wherein the identifiedmodulators of a biological system, e.g., determinative cellular processdriver (e.g., cellular cross-talk differentials or causal relationshipsunique in a biological system or process) may be markers/indicators orkey biological molecules causative of said disease or disorder, and mayin turn be used to identify biodefense agents.

In certain embodiments, the interrogative biological assessment is theidentification of targets for anti-aging agents, such as anti-agingcosmetics, wherein the identified modulators of a biological system,e.g., determinative cellular process driver (e.g., cellular cross-talkdifferentials or causal relationships unique in a biological system orprocess) may be markers or indicators of the aging process, particularlythe aging process in skin, and may in turn be used to identifyanti-aging agents.

In one exemplary cell model for aging that is used in the methods of theinvention to identify targets for anti-aging cosmetics, the cell modelcomprises an aging epithelial cell that is, for example, treated with UVlight (an environmental perturbation or external stimulus component),and/or neonatal cells, which are also optionally treated with UV light.In one embodiment, a cell model for aging comprises a cellularcross-talk system. In one exemplary two-cell cross-talk systemestablished to identify targets for anti-aging cosmetics, an agingepithelial cell (first cell system) may be treated with UV light (anexternal stimulus component), and changes, e.g., proteomic changesand/or functional changes, in a neonatal cell (second cell system)resulting from contacting the neonatal cells with conditioned medium ofthe treated aging epithelial cell may be measured, e.g., proteomechanges may be measured using conventional quantitative massspectrometry, or a causal relationship unique in aging may be identifiedfrom a causal relationship network generated from the data.

In another aspect, the invention provides a kit for conducting aninterrogative biological assessment using a discovery PlatformTechnology, comprising one or more reagents for detecting the presenceof, and/or for quantitating the amount of, an analyte that is thesubject of a causal relationship network generated from the methods ofthe invention. In one embodiment, said analyte is the subject of aunique causal relationship in the biological system, e.g., a geneassociated with a unique causal relationship in the biological system.In certain embodiments, the analyte is a protein, and the reagentscomprise an antibody against the protein, a label for the protein,and/or one or more agents for preparing the protein for high throughputanalysis (e.g., mass spectrometry based sequencing).

It should be understood that all embodiments described herein, includingthose described only in examples, are parts of the general descriptionof the invention, and can be combined with any other embodiments of theinvention unless explicitly disclaimed or inapplicable.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure will be described hereinbelow with reference to the figures wherein:

FIG. 1: Illustration of approach to identify therapeutics.

FIG. 2: Illustration of systems biology of cancer and consequence ofintegrated multi-physiological interactive output regulation.

FIG. 3: Illustration of systematic interrogation of biological relevanceusing MIMS.

FIG. 4: Illustration of modeling cancer network to enable interrogativebiological query.

FIG. 5: Illustration of the interrogative biology platform technology.

FIG. 6: Illustration of technologies employed in the platformtechnology.

FIG. 7: Schematic representation of the components of the platformincluding data collection, data integration, and data mining.

FIG. 8: Schematic representation of the systematic interrogation usingMIMS and collection of response data from the “omics” cascade.

FIG. 9: Sketch of the components employed to build the In vitro modelsrepresenting normal and diabetic states.

FIG. 10: Schematic representation of the informatics platform REFS™ usedto generate causal networks of the protein as they relate to diseasepathophysiology.

FIG. 11: Schematic representation of the approach towards generation ofdifferential network in diabetic versus normal states and diabetic nodesthat are restored to normal states by treatment with MIMS.

FIG. 12: A representative differential network in diabetic versus normalstates.

FIG. 13: A schematic representation of a node and associated edges ofinterest (Node1 in the center). The cellular functionality associatedwith each edge is represented.

FIG. 14: High level flow chart of an exemplary method, in accordancewith some embodiments.

FIG. 15A-15D: High level schematic illustration of the components andprocess for an AI-based informatics system that may be used withexemplary embodiments.

FIG. 16: Flow chart of process in AI-based informatics system that maybe used with some exemplary embodiments.

FIG. 17: Schematically depicts an exemplary computing environmentsuitable for practicing exemplary embodiments taught herein.

FIG. 18: Illustration of case study design described in Example 1.

FIG. 19: Effect of CoQ10 treatments on downstream nodes.

FIG. 20: CoQ10 treatment decreases expression of LDHA in cancer cellline HepG2.

FIG. 21: Exemplary protein interaction consensus network at 70% fragmentfrequency based on data from Paca2, HepG2 and THLE2 cell lines.

FIG. 22: Proteins responsive to LDHA expression simulation in two cancercell lines were identified using the platform technology.

FIG. 23: Ingenuity Pathway Assist® analysis of LDHA-PARK7 networkidentifies TP53 as upstream hub.

FIG. 24: Effect of CoQ10 treatment on TP53 expression levels in SKMEL28cancer cell line.

FIG. 25: Activation of TP53 associated with altered expression of BCL-2proteins effectuating apoptosis in SKMEL28 cancer cell line and effectof CoQ10 treatment on Bcl-2, Bax and Caspase3 expression levels inSKMEL28.

FIG. 26: Illustration of the mathematical approach towards generation ofdelta-delta networks.

FIG. 27: Cancer—Healthy differential (delta-delta) network that driveECAR and OCR. Each driver has differential effects on the end point asrepresented by the thickness of the edge. The thickness of the edge incytoscape represents the strength of the fold change.

FIG. 28: Mapping PARK7 and associated nodes from the interrogativeplatform technology outputs using IPA: The gray shapes include all thenodes associated with PARK7 from the interrogative biology outputs thatwere imported into IPA. The unfilled shapes (with names) are newconnections incorporated by IPA to create a complete map.

FIG. 29: The interrogative platform technology of the invention,demonstrating novel associations of nodes associated with PARK7. Edgesshown in dashed lines are connections between two nodes in thesimulations that have intermediate nodes, but do not have intermediatenodes in IPA. Edges shown in dotted lines are connections between twonodes in the simulations that have intermediate nodes, but havedifferent intermediate nodes in IPA.

FIG. 30: Illustration of the mathematical approach towards generation ofdelta-delta networks. Compare unique edges from NG in the NG∩HG deltanetwork with unique edges of HGT1 in the HG∩HGT1 delta network. Edges inthe intersection of NG and HGT1 are HG edges that are restored to NGwith T1.

FIG. 31: Delta-delta network of diabetic edges restored to normal withCoenzyme Q10 treatment superimposed on the NG∩HG delta network.

FIG. 32: Delta-delta network of hyperlipidemic edges restored to normalwith Coenzyme Q10 treatment superimposed on the normal lipidemia ∩ Hyperlipidemia delta network.

FIG. 33: A Schematic representing the altered fate of fatty acid indisease and drug treatment. A balance between utilization of free fattyacid (FFA) for generation of ATP and membrane remodeling in response todisruption of membrane biology has been implicated in drug inducedcardiotoxicity.

FIG. 34: A Schematic representing experimental design and modelingparameters used to study drug induced toxicity in diabeticcardiomyocytes.

FIG. 35: Dysregulation of transcriptional network and expression ofhuman mitochondrial energy metabolism genes in diabetic cardiomyocytesby drug treatment (T): rescue molecule (R) normalizes gene expression.

FIG. 36: A. Drug treatment (T) induced expression of GPAT1 and TAZ inmitochondria from cardiomyocytes conditioned in hyperglycemia. Incombination with the rescue molecule (T+R) the levels of GPAT1 and TAZwere normalized. B. Synthesis of TAG from G3P.

FIG. 37: A. Drug treatment (T) decreases mitochondrial OCR (oxygenconsumption rate) in cardiomyocytes conditioned in hyperglycemia. Therescue molecule (T+R) normalizes OCR. B. Drug treatment (T) repressesmitochondrial ATP synthesis in cardiomyocytes conditioned inhyperglycemia.

FIG. 38: GO Annotation of proteins down regulated by drug treatment.Proteins involved in mitochondrial energy metabolism were down regulatedwith drug treatment.

FIG. 39: Illustration of the mathematical approach towards generation ofdelta networks. Compare unique edges from T versus UT both the modelsbeing in diabetic environment.

FIG. 40: A schematic representing potential protein hubs and networksthat drive pathophysiology of drug induced toxicity.

DETAILED DESCRIPTION OF THE INVENTION I. Overview

Exemplary embodiments of the present invention incorporate methods thatmay be performed using an interrogative biology platform (“thePlatform”) that is a tool for understanding a wide variety of biologicalprocesses, such as disease pathophysiology, and the key moleculardrivers underlying such biological processes, including factors thatenable a disease process. Some exemplary embodiments include systemsthat may incorporate at least a portion of, or all of, the Platform.Some exemplary methods may employ at least some of, or all of thePlatform. Goals and objectives of some exemplary embodiments involvingthe platform are generally outlined below for illustrative purposes:

i) to create specific molecular signatures as drivers of criticalcomponents of the biological process (e.g., disease process) as theyrelate to overall pathophysiology of the biological process;

ii) to generate molecular signatures or differential maps pertaining tothe biological process, which may help to identify differentialmolecular signatures that distinguishes one biological state (e.g., adisease state) versus a different biological stage (e.g., a normalstate), and develop understanding of signatures or molecular entities asthey arbitrate mechanisms of change between the two biological states(e.g., from normal to disease state); and,

iii) to investigate the role of “hubs” of molecular activity aspotential intervention targets for external control of the biologicalprocess (e.g., to use the hub as a potential therapeutic target), or aspotential bio-markers for the biological process in question (e.g.,disease specific biomarkers, in prognostic and/or theranostics uses).

Some exemplary methods involving the Platform may include one or more ofthe following features:

1) modeling the biological process (e.g., disease process) and/orcomponents of the biological process (e.g., disease physiology &pathophysiology) in one or more models, preferably in vitro models,using cells associated with the biological process. For example, thecells may be human derived cells which normally participate in thebiological process in question. The model may include various cellularcues/conditions/perturbations that are specific to the biologicalprocess (e.g., disease). Ideally, the model represents various (disease)states and flux components, instead of a static assessment of thebiological (disease) condition.

2) profiling mRNA and/or protein signatures using any art-recognizedmeans. For example, quantitative polymerase chain reaction (qPCR) &proteomics analysis tools such as Mass Spectrometry (MS). Such mRNA andprotein data sets represent biological reaction toenvironment/perturbation. Where applicable and possible, lipidomics,metabolomics, and transcriptomics data may also be integrated assupplemental or alternative measures for the biological process inquestion. SNP analysis is another component that may be used at times inthe process. It may be helpful for investigating, for example, whetherthe SNP or a specific mutation has any effect on the biological process.These variables may be used to describe the biological process, eitheras a static “snapshot,” or as a representation of a dynamic process.

3) assaying for one or more cellular responses to cues andperturbations, including but not limited to bioenergetics profiling,cell proliferation, apoptosis, and organellar function. Truegenotype-phenotype association is actualized by employment of functionalmodels, such as ATP, ROS, OXPHOS, Seahorse assays, etc. Such cellularresponses represent the reaction of the cells in the biological process(or models thereof) in response to the corresponding state(s) of themRNA/protein expression, and any other related states in 2) above.

4) integrating functional assay data thus obtained in 3) with proteomicsand other data obtained in 2), and determining protein associations asdriven by causality, by employing artificial intelligence based(AI-based) informatics system or platform. Such an AI-based system isbased on, and preferably based only on, the data sets obtained in 2)and/or 3), without resorting to existing knowledge concerning thebiological process. Preferably, no data points are statistically orartificially cut-off. Instead, all obtained data is fed into theAI-system for determining protein associations. One goal or output ofthe integration process is one or more differential networks (otherwisemay be referred to herein as “delta networks,” or, in some cases,“delta-delta networks” as the case may be) between the differentbiological states (e.g., disease vs. normal states).

5) profiling the outputs from the AI-based informatics platform toexplore each hub of activity as a potential therapeutic target and/orbiomarker. Such profiling can be done entirely in silico based on theobtained data sets, without resorting to any actual wet-lab experiments.

6) validating hub of activity by employing molecular and cellulartechniques. Such post-informatic validation of output with wet-labcell-based experiments may be optional, but they help to create afull-circle of interrogation.

Any or all of the approaches outlined above may be used in any specificapplication concerning any biological process, depending, at least inpart, on the nature of the specific application. That is, one or moreapproaches outlined above may be omitted or modified, and one or moreadditional approaches may be employed, depending on specificapplication.

Various schematics illustrating the platform are provided. Inparticular, an illustration of an exemplary approach to identifytherapeutics using the platform is depicted in FIG. 1. An illustrationof systems biology of cancer and the consequence of integratedmulti-physiological interactive output regulation is depicted in FIG. 2.An illustration of a systematic interrogation of biological relevanceusing MIMS is depicted in FIG. 3. An illustration of modeling a cancernetwork to enable an interrogative biological query is depicted in FIG.4.

Illustrations of the interrogative biology platform and technologiesemployed in the platform are depicted in FIGS. 5 and 6. A schematicrepresentation of the components of the platform including datacollection, data integration, and data mining is depicted in FIG. 7. Aschematic representation of a systematic interrogation using MIMS andcollection of response data from the “omics” cascade is depicted in FIG.8.

FIG. 14 is a high level flow chart of an exemplary method 10, in whichcomponents of an exemplary system that may be used to perform theexemplary method are indicated. Initially, a model (e.g., an in vitromodel) is established for a biological process (e.g., a disease process)and/or components of the biological process (e.g., disease physiologyand pathophysiology) using cells normally associated with the biologicalprocess (step 12). For example, the cells may be human-derived cellsthat normally participate in the biological process (e.g., disease). Thecell model may include various cellular cues, conditions, and/orperturbations that are specific to the biological process (e.g.,disease). Ideally, the cell model represents various (disease) statesand flux components of the biological process (e.g., disease), insteadof a static assessment of the biological process. The comparison cellmodel may include control cells or normal (e.g., non-diseased) cells.Additional description of the cell models appears below in sectionsIII.A and IV.

A first data set is obtained from the cell model for the biologicalprocess, which includes information representing expression levels of aplurality of genes (e.g., mRNA and/or protein signatures) (step 16)using any known process or system (e.g., quantitative polymerase chainreaction (qPCR) & proteomics analysis tools such as Mass Spectrometry(MS)).

A third data set is obtained from the comparison cell model for thebiological process (step 18). The third data set includes informationrepresenting expression levels of a plurality of genes in the comparisoncells from the comparison cell model.

In certain embodiments of the methods of the invention, these first andthird data sets are collectively referred to herein as a “first dataset” that represents expression levels of a plurality of genes in thecells (all cells including comparison cells) associated with thebiological system.

The first data set and third data set may be obtained from one or moremRNA and/or Protein Signature Analysis System(s). The mRNA and proteindata in the first and third data sets may represent biological reactionsto environment and/or perturbation. Where applicable and possible,lipidomics, metabolomics, and transcriptomics data may also beintegrated as supplemental or alternative measures for the biologicalprocess. The SNP analysis is another component that may be used at timesin the process. It may be helpful for investigating, for example,whether a single-nucleotide polymorphism (SNP) or a specific mutationhas any effect on the biological process. The data variables may be usedto describe the biological process, either as a static “snapshot,” or asa representation of a dynamic process. Additional description regardingobtaining information representing expression levels of a plurality ofgenes in cells appears below in section III.B.

A second data set is obtained from the cell model for the biologicalprocess, which includes information representing a functional activityor response of cells (step 20). Similarly, a fourth data set is obtainedfrom the comparison cell model for the biological process, whichincludes information representing a functional activity or response ofthe comparison cells (step 22).

In certain embodiments of the methods of the invention, these second andfourth data sets are collectively referred to herein as a “second dataset” that represents a functional activity or a cellular response of thecells (all cells including comparison cells) associated with thebiological system.

One or more functional assay systems may be used to obtain informationregarding the functional activity or response of cells or of comparisoncells. The information regarding functional cellular responses to cuesand perturbations may include, but is not limited to, bioenergeticsprofiling, cell proliferation, apoptosis, and organellar function.Functional models for processes and pathways (e.g., adenosinetriphosphate (ATP), reactive oxygen species (ROS), oxidativephosphorylation (OXPHOS), Seahorse assays, etc.) may be employed toobtain true genotype-phenotype association. The functional activity orcellular responses represent the reaction of the cells in the biologicalprocess (or models thereof) in response to the corresponding state(s) ofthe mRNA/protein expression, and any other related applied conditions orperturbations. Additional information regarding obtaining informationrepresenting functional activity or response of cells is provided belowin section III.B.

The method also includes generating computer-implemented models of thebiological processes in the cells and in the control cells. For example,one or more (e.g., an ensemble of) Bayesian networks of causalrelationships between the expression level of the plurality of genes andthe functional activity or cellular response may be generated for thecell model (the “generated cell model networks”) from the first data setand the second data set (step 24). The generated cell model networks,individually or collectively, include quantitative probabilisticdirectional information regarding relationships. The generated cellmodel networks are not based on known biological relationships betweengene expression and/or functional activity or cellular response, otherthan information from the first data set and second data set. The one ormore generated cell model networks may collectively be referred to as aconsensus cell model network.

One or more (e.g., an ensemble of) Bayesian networks of causalrelationships between the expression level of the plurality of genes andthe functional activity or cellular response may be generated for thecomparison cell model (the “generated comparison cell model networks”)from the first data set and the second data set (step 26). The generatedcomparison cell model networks, individually or collectively, includequantitative probabilistic directional information regardingrelationships. The generated cell networks are not based on knownbiological relationships between gene expression and/or functionalactivity or cellular response, other than the information in the firstdata set and the second data set. The one or more generated comparisonmodel networks may collectively be referred to as a consensus cell modelnetwork.

The generated cell model networks and the generated comparison cellmodel networks may be created using an artificial intelligence based(AI-based) informatics platform. Further details regarding the creationof the generated cell model networks, the creation of the generatedcomparison cell model networks and the AI-based informatics systemappear below in section III.C and in the description of FIGS. 2A-3.

It should be noted that many different AI-based platforms or systems maybe employed to generate the Bayesian networks of causal relationshipsincluding quantitative probabilistic directional information. Althoughcertain examples described herein employ one specific commerciallyavailable system, i.e., REFS™ (Reverse Engineering/Forward Simulation)from GNS (Cambridge, Mass.), embodiments are not limited. AI-BasedSystems or Platforms suitable to implement some embodiments employmathematical algorithms to establish causal relationships among theinput variables (e.g., the first and second data sets), based only onthe input data without taking into consideration prior existingknowledge about any potential, established, and/or verified biologicalrelationships.

For example, the REFS™ AI-based informatics platform utilizesexperimentally derived raw (original) or minimally processed inputbiological data (e.g., genetic, genomic, epigenetic, proteomic,metabolomic, and clinical data), and rapidly performs trillions ofcalculations to determine how molecules interact with one another in acomplete system. The REFS™ AI-based informatics platform performs areverse engineering process aimed at creating an in silicocomputer-implemented cell model (e.g., generated cell model networks),based on the input data, that quantitatively represents the underlyingbiological system. Further, hypotheses about the underlying biologicalsystem can be developed and rapidly simulated based on thecomputer-implemented cell model, in order to obtain predictions,accompanied by associated confidence levels, regarding the hypotheses.

With this approach, biological systems are represented by quantitativecomputer-implemented cell models in which “interventions” are simulatedto learn detailed mechanisms of the biological system (e.g., disease),effective intervention strategies, and/or clinical biomarkers thatdetermine which patients will respond to a given treatment regimen.Conventional bioinformatics and statistical approaches, as well asapproaches based on the modeling of known biology, are typically unableto provide these types of insights.

After the generated cell model networks and the generated comparisoncell model networks are created, they are compared. One or more causalrelationships present in at least some of the generated cell modelnetworks, and absent from, or having at least one significantlydifferent parameter in, the generated comparison cell model networks areidentified (step 28). Such a comparison may result in the creation of adifferential network. The comparison, identification, and/ordifferential (delta) network creation may be conducted using adifferential network creation module, which is described in furtherdetail below in section III.D and with respect to the description ofFIG. 26.

In some embodiments, input data sets are from one cell type and onecomparison cell type, which creates an ensemble of cell model networksbased on the one cell type and another ensemble of comparison cell modelnetworks based on the one comparison control cell type. A differentialmay be performed between the ensemble of networks of the one cell typeand the ensemble of networks of the comparison cell type(s).

In other embodiments, input data sets are from multiple cell types(e.g., two or more cancer cell types) and multiple comparison cell types(e.g., two or more normal, non-cancerous cell types). An ensemble ofcell model networks may be generated for each cell types and eachcomparison cell type individually, and/or data from the multiple celltypes and the multiple comparison cell types may be combined intorespective composite data sets. The composite data sets produce anensemble of networks corresponding to the multiple cell types (compositedata) and another ensemble of networks corresponding to the multiplecomparison cell types (comparison composite data). A differential may beperformed on the ensemble of networks for the composite data as comparedto the ensemble of networks for the comparison composite data.

In some embodiments, a differential may be performed between twodifferent differential networks. This output may be referred to as adelta-delta network, and is described below with respect to FIG. 26.

Quantitative relationship information may be identified for eachrelationship in the generated cell model networks (step 30). Similarly,quantitative relationship information for each relationship in thegenerated comparison cell model networks may be identified (step 32).The quantitative information regarding the relationship may include adirection indicating causality, a measure of the statistical uncertaintyregarding the relationship (e.g., an Area Under the Curve (AUC)statistical measurement), and/or an expression of the quantitativemagnitude of the strength of the relationship (e.g., a fold). Thevarious relationships in the generated cell model networks may beprofiled using the quantitative relationship information to explore eachhub of activity in the networks as a potential therapeutic target and/orbiomarker. Such profiling can be done entirely in silico based on theresults from the generated cell model networks, without resorting to anyactual wet-lab experiments.

In some embodiments, a hub of activity in the networks may be validatedby employing molecular and cellular techniques. Such post-informaticvalidation of output with wet-lab cell based experiments need not beperformed, but it may help to create a full-circle of interrogation.FIG. 15 schematically depicts a simplified high level representation ofthe functionality of an exemplary AI-based informatics system (e.g.,REFS™ AI-based informatics system) and interactions between the AI-basedsystem and other elements or portions of an interrogative biologyplatform (“the Platform”). In FIG. 15A, various data sets obtained froma model for a biological process (e.g., a disease model), such as drugdosage, treatment dosage, protein expression, mRNA expression, and anyof many associated functional measures (such as OCR, ECAR) are fed intoan AI-based system. As shown in FIG. 15B, from the input data sets, theAI-system creates a library of “network fragments” that includesvariables (proteins, lipids and metabolites) that drive molecularmechanisms in the biological process (e.g., disease), in a processreferred to as Bayesian Fragment Enumeration (FIG. 15B).

In FIG. 15C, the AI-based system selects a subset of the networkfragments in the library and constructs an initial trial network fromthe fragments. The AI-based system also selects a different subset ofthe network fragments in the library to construct another initial trialnetwork. Eventually an ensemble of initial trial networks are created(e.g., 1000 networks) from different subsets of network fragments in thelibrary. This process may be termed parallel ensemble sampling. Eachtrial network in the ensemble is evolved or optimized by adding,subtracting and/or substitution additional network fragments from thelibrary. If additional data is obtained, the additional data may beincorporated into the network fragments in the library and may beincorporated into the ensemble of trial networks through the evolutionof each trial network. After completion of the optimization/evolutionprocess, the ensemble of trial networks may be described as thegenerated cell model networks.

As shown in FIG. 15D, the ensemble of generated cell model networks maybe used to simulate the behavior of the biological system. Thesimulation may be used to predict behavior of the biological system tochanges in conditions, which may be experimentally verified usingwet-lab cell-based, or animal-based, experiments. Also, quantitativeparameters of relationships in the generated cell model networks may beextracted using the simulation functionality by applying simulatedperturbations to each node individually while observing the effects onthe other nodes in the generated cell model networks. Further detail isprovided below in section III.C.

The automated reverse engineering process of the AI-based informaticssystem, which is depicted in FIGS. 2A-2D, creates an ensemble ofgenerated cell model networks networks that is an unbiased andsystematic computer-based model of the cells.

The reverse engineering determines the probabilistic directional networkconnections between the molecular measurements in the data, and thephenotypic outcomes of interest. The variation in the molecularmeasurements enables learning of the probabilistic cause and effectrelationships between these entities and changes in endpoints. Themachine learning nature of the platform also enables cross training andpredictions based on a data set that is constantly evolving.

The network connections between the molecular measurements in the dataare “probabilistic,” partly because the connection may be based oncorrelations between the observed data sets “learned” by the computeralgorithm. For example, if the expression level of protein X and that ofprotein Y are positively or negatively correlated, based on statisticalanalysis of the data set, a causal relationship may be assigned toestablish a network connection between proteins X and Y. The reliabilityof such a putative causal relationship may be further defined by alikelihood of the connection, which can be measured by p-value (e.g.,p<0.1, 0.05, 0.01, etc).

The network connections between the molecular measurements in the dataare “directional,” partly because the network connections between themolecular measurements, as determined by the reverse-engineeringprocess, reflects the cause and effect of the relationship between theconnected gene/protein, such that raising the expression level of oneprotein may cause the expression level of the other to rise or fall,depending on whether the connection is stimulatory or inhibitory.

The network connections between the molecular measurements in the dataare “quantitative,” partly because the network connections between themolecular measurements, as determined by the process, may be simulatedin silico, based on the existing data set and the probabilistic measuresassociated therewith. For example, in the established networkconnections between the molecular measurements, it may be possible totheoretically increase or decrease (e.g., by 1, 2, 3, 5, 10, 20, 30, 50,100-fold or more) the expression level of a given protein (or a “node”in the network), and quantitatively simulate its effects on otherconnected proteins in the network.

The network connections between the molecular measurements in the dataare “unbiased,” at least partly because no data points are statisticallyor artificially cut-off, and partly because the network connections arebased on input data alone, without referring to pre-existing knowledgeabout the biological process in question.

The network connections between the molecular measurements in the dataare “systemic” and (unbiased), partly because all potential connectionsamong all input variables have been systemically explored, for example,in a pair-wise fashion. The reliance on computing power to execute suchsystemic probing exponentially increases as the number of inputvariables increases.

In general, an ensemble of ˜1,000 networks is usually sufficient topredict probabilistic causal quantitative relationships among all of themeasured entities. The ensemble of networks captures uncertainty in thedata and enables the calculation of confidence metrics for each modelprediction. Predictions generated using the ensemble of networkstogether, where differences in the predictions from individual networksin the ensemble represent the degree of uncertainty in the prediction.This feature enables the assignment of confidence metrics forpredictions of clinical response generated from the model.

Once the models are reverse-engineered, further simulation queries maybe conducted on the ensemble of models to determine key moleculardrivers for the biological process in question, such as a diseasecondition.

Sketch of components employed to build exemplary In vitro modelsrepresenting normal and diabetic statesis is depicted in FIG. 9.Schematic representation of an exemplary informatics platform REFS™ usedto generate causal networks of the protein as they relate to diseasepathophysiology is depicted in FIG. 10. Schematic representation ofexemplary approach towards generation of differential network indiabetic versus normal states and diabetic nodes that are restored tonormal states by treatment with MIMS is depicted in FIG. 11. Arepresentative differential network in diabetic versus normal states isdepicted in FIG. 12. A schematic representation of a node and associatededges of interest (Node1 in the center) and the cellular functionalityassociated with each edge is depicted in FIG. 13.

The invention having been generally described above, the sections belowprovide more detailed description for various aspects or elements of thegeneral invention, in conjunction with one or more specific biologicalsystems that can be analyzed using the methods herein. It should benoted, however, the specific biological systems used for illustrationpurpose below are not limiting. To the contrary, it is intended thatother distinct biological systems, including any alternatives,modifications, and equivalents thereof, may be analyzed similarly usingthe subject Platform technology.

II. Definitions

As used herein, certain terms intended to be specifically defined, butare not already defined in other sections of the specification, aredefined herein.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

The term “including” is used herein to mean, and is used interchangeablywith, the phrase “including but not limited to.”

The term “or” is used herein to mean, and is used interchangeably with,the term “and/or,” unless context clearly indicates otherwise.

The term “such as” is used herein to mean, and is used interchangeably,with the phrase “such as but not limited to.”

“Metabolic pathway” refers to a sequence of enzyme-mediated reactionsthat transform one compound to another and provide intermediates andenergy for cellular functions. The metabolic pathway can be linear orcyclic or branched.

“Metabolic state” refers to the molecular content of a particularcellular, multicellular or tissue environment at a given point in timeas measured by various chemical and biological indicators as they relateto a state of health or disease.

The term “microarray” refers to an array of distinct polynucleotides,oligonucleotides, polypeptides (e.g., antibodies) or peptidessynthesized on a substrate, such as paper, nylon or other type ofmembrane, filter, chip, glass slide, or any other suitable solidsupport.

The terms “disorders” and “diseases” are used inclusively and refer toany deviation from the normal structure or function of any part, organor system of the body (or any combination thereof). A specific diseaseis manifested by characteristic symptoms and signs, includingbiological, chemical and physical changes, and is often associated witha variety of other factors including, but not limited to, demographic,environmental, employment, genetic and medically historical factors.Certain characteristic signs, symptoms, and related factors can bequantitated through a variety of methods to yield important diagnosticinformation.

The term “expression” includes the process by which a polypeptide isproduced from polynucleotides, such as DNA. The process may involves thetranscription of a gene into mRNA and the translation of this mRNA intoa polypeptide. Depending on the context in which it is used,“expression” may refer to the production of RNA, protein or both.

The terms “level of expression of a gene” or “gene expression level”refer to the level of mRNA, as well as pre-mRNA nascent transcript(s),transcript processing intermediates, mature mRNA(s) and degradationproducts, or the level of protein, encoded by the gene in the cell.

The term “modulation” refers to upregulation (i.e., activation orstimulation), downregulation (i.e., inhibition or suppression) of aresponse, or the two in combination or apart. A “modulator” is acompound or molecule that modulates, and may be, e.g., an agonist,antagonist, activator, stimulator, suppressor, or inhibitor.

The term “Trolamine,” as used herein, refers to Trolamine NF,Triethanolamine, TEALAN®, TEAlan 99%, Triethanolamine, 99%,Triethanolamine, NF or Triethanolamine, 99%, NF. These terms may be usedinterchangeably herein.

The term “genome” refers to the entirety of a biological entity's (cell,tissue, organ, system, organism) genetic information. It is encodedeither in DNA or RNA (in certain viruses, for example). The genomeincludes both the genes and the non-coding sequences of the DNA.

The term “proteome” refers to the entire set of proteins expressed by agenome, a cell, a tissue, or an organism at a given time. Morespecifically, it may refer to the entire set of expressed proteins in agiven type of cells or an organism at a given time under definedconditions. Proteome may include protein variants due to, for example,alternative splicing of genes and/or post-translational modifications(such as glycosylation or phosphorylation).

The term “transcriptome” refers to the entire set of transcribed RNAmolecules, including mRNA, rRNA, tRNA, and other non-coding RNA producedin one or a population of cells at a given time. The term can be appliedto the total set of transcripts in a given organism, or to the specificsubset of transcripts present in a particular cell type. Unlike thegenome, which is roughly fixed for a given cell line (excludingmutations), the transcriptome can vary with external environmentalconditions. Because it includes all mRNA transcripts in the cell, thetranscriptome reflects the genes that are being actively expressed atany given time, with the exception of mRNA degradation phenomena such astranscriptional attenuation.

The study of transcriptomics, also referred to as expression profiling,examines the expression level of mRNAs in a given cell population, oftenusing high-throughput techniques based on DNA microarray technology.

The term “metabolome” refers to the complete set of small-moleculemetabolites (such as metabolic intermediates, hormones and othersignalling molecules, and secondary metabolites) to be found within abiological sample, such as a single organism, at a given time under agiven condition. The metabolome is dynamic, and may change from secondto second.

The term “lipidome” refers to the complete set of lipids to be foundwithin a biological sample, such as a single organism, at a given timeunder a given condition. The lipidome is dynamic, and may change fromsecond to second.

The term “interactome” refers to the whole set of molecular interactionsin a biological system under study (e.g., cells). It can be displayed asa directed graph. Molecular interactions can occur between moleculesbelonging to different biochemical families (proteins, nucleic acids,lipids, carbohydrates, etc.) and also within a given family. When spokenin terms of proteomics, interactome refers to protein-proteininteraction network (PPI), or protein interaction network (PIN). Anotherextensively studied type of interactome is the protein-DNA interactome(network formed by transcription factors (and DNA or chromatinregulatory proteins) and their target genes.

The term “cellular output” includes a collection of parameters,preferably measurable parameters, relating to cellullar status,including (without limiting): level of transcription for one or moregenes (e.g., measurable by RT-PCR, qPCR, microarray, etc.), level ofexpression for one or more proteins (e.g., measurable by massspectrometry or Western blot), absolute activity (e.g., measurable assubstrate conversion rates) or relative activity (e.g., measurable as a% value compared to maximum activity) of one or more enzymes orproteins, level of one or more metabolites or intermediates, level ofoxidative phosphorylation (e.g., measurable by Oxygen Consumption Rateor OCR), level of glycolysis (e.g., measurable by Extra CellularAcidification Rate or ECAR), extent of ligand-target binding orinteraction, activity of extracellular secreted molecules, etc. Thecellular output may include data for a pre-determined number of targetgenes or proteins, etc., or may include a global assessment for alldetectable genes or proteins. For example, mass spectrometry may be usedto identify and/or quantitate all detectable proteins expressed in agiven sample or cell population, without prior knowledge as to whetherany specific protein may be expressed in the sample or cell population.

As used herein, a “cell system” includes a population of homogeneous orheterogeneous cells. The cells within the system may be growing in vivo,under the natural or physiological environment, or may be growing invitro in, for example, controlled tissue culture environments. The cellswithin the system may be relatively homogeneous (e.g., no less than 70%,80%, 90%, 95%, 99%, 99.5%, 99.9% homogeneous), or may contain two ormore cell types, such as cell types usually found to grow in closeproximity in vivo, or cell types that may interact with one another invivo through, e.g., paracrine or other long distance inter-cellularcommunication. The cells within the cell system may be derived fromestablished cell lines, including cancer cell lines, immortal celllines, or normal cell lines, or may be primary cells or cells freshlyisolated from live tissues or organs.

Cells in the cell system are typically in contact with a “cellularenvironment” that may provide nutrients, gases (oxygen or CO₂, etc.),chemicals, or proteinaceous/non-proteinaceous stimulants that may definethe conditions that affect cellular behavior. The cellular environmentmay be a chemical media with defined chemical components and/or lesswell-defined tissue extracts or serum components, and may include aspecific pH, CO₂ content, pressure, and temperature under which thecells grow. Alternatively, the cellular environment may be the naturalor physiological environment found in vivo for the specific cell system.

In certain embodiments, a cell environment comprises conditions thatsimulate an aspect of a biological system or process, e.g., simulate adisease state, process, or environment. Such culture conditions include,for example, hyperglycemia, hypoxia, or lactic-rich conditions. Numerousother such conditions are described herein.

In certain embodiments, a cellular environment for a specific cellsystem also include certain cell surface features of the cell system,such as the types of receptors or ligands on the cell surface and theirrespective activities, the structure of carbohydrate or lipid molecules,membrane polarity or fluidity, status of clustering of certain membraneproteins, etc. These cell surface features may affect the function ofnearby cells, such as cells belonging to a different cell system. Incertain other embodiments, however, the cellular environment of a cellsystem does not include cell surface features of the cell system.

The cellular environment may be altered to become a “modified cellularenvironment.” Alterations may include changes (e.g., increase ordecrease) in any one or more component found in the cellularenvironment, including addition of one or more “external stimuluscomponent” to the cellular environment. The environmental perturbationor external stimulus component may be endogenous to the cellularenvironment (e.g., the cellular environment contains some levels of thestimulant, and more of the same is added to increase its level), or maybe exogenous to the cellular environment (e.g., the stimulant is largelyabsent from the cellular environment prior to the alteration). Thecellular environment may further be altered by secondary changesresulting from adding the external stimulus component, since theexternal stimulus component may change the cellular output of the cellsystem, including molecules secreted into the cellular environment bythe cell system.

As used herein, “external stimulus component”, also referred to hereinas “environmental perturbation”, include any external physical and/orchemical stimulus that may affect cellular function. This may includeany large or small organic or inorganic molecules, natural or syntheticchemicals, temperature shift, pH change, radiation, light (UVA, UVBetc.), microwave, sonic wave, electrical current, modulated orunmodulated magnetic fields, etc.

The term “Multidimensional Intracellular Molecule (MIM)”, is an isolatedversion or synthetically produced version of an endogenous molecule thatis naturally produced by the body and/or is present in at least one cellof a human. A MIM is capable of entering a cell and the entry into thecell includes complete or partial entry into the cell as long as thebiologically active portion of the molecule wholly enters the cell. MIMsare capable of inducing a signal transduction and/or gene expressionmechanism within a cell. MIMs are multidimensional because the moleculeshave both a therapeutic and a carrier, e.g., drug delivery, effect. MIMsalso are multidimensional because the molecules act one way in a diseasestate and a different way in a normal state. For example, in the case ofCoQ-10, administration of CoQ-10 to a melanoma cell in the presence ofVEGF leads to a decreased level of Bcl2 which, in turn, leads to adecreased oncogenic potential for the melanoma cell. In contrast, in anormal fibroblast, co-administration of CoQ-10 and VEFG has no effect onthe levels of Bcl2.

In one embodiment, a MIM is also an epi-shifter In another embodiment, aMIM is not an epi-shifter. In another embodiment, a MIM is characterizedby one or more of the foregoing functions. In another embodiment, a MIMis characterized by two or more of the foregoing functions. In a furtherembodiment, a MIM is characterized by three or more of the foregoingfunctions. In yet another embodiment, a MIM is characterized by all ofthe foregoing functions. The skilled artisan will appreciate that a MIMof the invention is also intended to encompass a mixture of two or moreendogenous molecules, wherein the mixture is characterized by one ormore of the foregoing functions. The endogenous molecules in the mixtureare present at a ratio such that the mixture functions as a MIM.

MIMs can be lipid based or non-lipid based molecules. Examples of MIMsinclude, but are not limited to, CoQ10, acetyl Co-A, palmityl Co-A,L-carnitine, amino acids such as, for example, tyrosine, phenylalanine,and cysteine. In one embodiment, the MIM is a small molecule. In oneembodiment of the invention, the MIM is not CoQ10. MIMs can be routinelyidentified by one of skill in the art using any of the assays describedin detail herein. MIMs are described in further detail in U.S. Ser. No.12/777,902 (US 2011-0110914), the entire contents of which are expresslyincorporated herein by reference.

As used herein, an “epimetabolic shifter” (epi-shifter) is a moleculethat modulates the metabolic shift from a healthy (or normal) state to adisease state and vice versa, thereby maintaining or reestablishingcellular, tissue, organ, system and/or host health in a human.Epi-shifters are capable of effectuating normalization in a tissuemicroenvironment. For example, an epi-shifter includes any moleculewhich is capable, when added to or depleted from a cell, of affectingthe microenvironment (e.g., the metabolic state) of a cell. The skilledartisan will appreciate that an epi-shifter of the invention is alsointended to encompass a mixture of two or more molecules, wherein themixture is characterized by one or more of the foregoing functions. Themolecules in the mixture are present at a ratio such that the mixturefunctions as an epi-shifter. Examples of epi-shifters include, but arenot limited to, CoQ-10; vitamin D3; ECM components such as fibronectin;immunomodulators, such as TNFa or any of the interleukins, e.g., IL-5,IL-12, IL-23; angiogenic factors; and apoptotic factors.

In one embodiment, the epi-shifter also is a MIM. In one embodiment, theepi-shifter is not CoQ10. Epi-shifters can be routinely identified byone of skill in the art using any of the assays described in detailherein. Epi-shifters are described in further detail in U.S. Ser. No.12/777,902 (US 2011-0110914), the entire contents of which are expresslyincorporated herein by reference.

Other terms not explicitly defined in the instant application havemeaning as would have been understood by one of ordinary skill in theart.

III. Exemplary Steps and Components of the Platform Technology

For illustration purpose only, the following steps of the subjectPlatform Technology may be described herein below as an exemplaryutility for integrating data obtained from a custom built cancer model,and for identifying novel proteins/pathways driving the pathogenesis ofcancer. Relational maps resulting from this analysis provides cancertreatment targets, as well as diagnostic/prognostic markers associatedwith cancer. However, the subject Platform Technology has generalapplicability for any biological system or process, and is not limitedto any particular cancer or other specific disease models.

In addition, although the description below is presented in someportions as discrete steps, it is for illustration purpose andsimplicity, and thus, in reality, it does not imply such a rigid orderand/or demarcation of steps. Moreover, the steps of the invention may beperformed separately, and the invention provided herein is intended toencompass each of the individual steps separately, as well ascombinations of one or more (e.g., any one, two, three, four, five, sixor all seven steps) steps of the subject Platform Technology, which maybe carried out independently of the remaining steps.

The invention also is intended to include all aspects of the PlatformTechnology as separate components and embodiments of the invention. Forexample, the generated data sets are intended to be embodiments of theinvention. As further examples, the generated causal relationshipnetworks, generated consensus causal relationship networks, and/orgenerated simulated causal relationship networks, are also intended tobe embodiments of the invention. The causal relationships identified asbeing unique in the biological system are intended to be embodiments ofthe invention. Further, the custom built models for a particularbiological system are also intended to be embodiments of the invention.For example, custom built models for a disease state or process, suchas, e.g., cell models for cancer, obesity/diabetes/cardiovasculardisease, or a custom built model for toxicity (e.g., cardiotoxicity) ofa drug, are also intended to be embodiments of the invention.

A. Custom Model Building

The first step in the Platform Technology is the establishment of amodel for a biological system or process. An example of a biologicalsystem or process is cancer. As any other complicated biological processor system, cancer is a complicated pathological condition characterizedby multiple unique aspects. For example, due to its high growth rate,many cancer cells are adapted to grow in hypoxia conditions, haveup-regulated glycolysis and reduced oxidative phosphorylation metabolicpathways. As a result, cancer cells may react differently to anenvironmental perturbation, such as treatment by a potential drug, ascompared to the reaction by a normal cell in response to the sametreatment. Thus, it would be of interest to decipher cancer's uniqueresponses to drug treatment as compared to the responses of normalcells. To this end, a custom cancer model may be established to simulatethe environment of a cancer cell, e.g., within a tumor in vivo, bycreating cell culture conditions closely approximating the conditions ofa cancer cell in a tumor in vivo, or to mimic various aspects of cancergrowth, by isolating different growth conditions of the cancer cells.

One such cancer “environment”, or growth stress condition, is hypoxia, acondition typically found within a solid tumor. Hypoxia can be inducedin cells in cells using art-recognized methods. For example, hypoxia canbe induced by placing cell systems in a Modular Incubator Chamber(MIC-101, Billups-Rothenberg Inc. Del Mar, Calif.), which can be floodedwith an industrial gas mix containing 5% CO₂, 2% O₂ and 93% nitrogen.Effects can be measured after a pre-determined period, e.g., at 24 hoursafter hypoxia treatment, with and without additional external stimuluscomponents (e.g., CoQ10 at 0, 50, or 100 μM).

Likewise, lactic acid treatment of cells mimics a cellular environmentwhere glycolysis activity is high, as exists in the tumor environment invivo. Lactic acid induced stress can be investigated at a final lacticacid concentration of about 12.5 mM at a pre-determined time, e.g., at24 hours, with or without additional external stimulus components (e.g.,CoQ10 at 0, 50, or 100 μM).

Hyperglycemia is normally a condition found in diabetes; however,hyperglycemia also to some extent mimics one aspect of cancer growthbecause many cancer cells rely on glucose as their primary source ofenergy. Exposing subject cells to a typical hyperglycemic condition mayinclude adding 10% culture grade glucose to suitable media, such thatthe final concentration of glucose in the media is about 22 mM.

Individual conditions reflecting different aspects of cancer growth maybe investigated separately in the custom built cancer model, and/or maybe combined together. In one embodiment, combinations of at least 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more conditionsreflecting or simulating different aspects of cancer growth/conditionsare investigated in the custom built cancer model. In one embodiment,individual conditions and, in addition, combinations of at least 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more of the conditionsreflecting or simulating different aspects of cancer growth/conditionsare investigated in the custom built cancer model. All values presentedin the foregoing list can also be the upper or lower limit of ranges,that are intended to be a part of this invention, e.g., between 1 and 5,1 and 10, 1 and 20, 1 and 30, 2 and 5, 2 and 10, 5 and 10, 1 and 20, 5and 20, and 20, 10 and 25, 10 and 30 or 10 and 50 different conditions.

Listed herein below are a few exemplary combinations of conditions thatcan be used to treat cells. Other combinations can be readily formulateddepending on the specific interrogative biological assessment that isbeing conducted.

-   -   1. Media only    -   2. 50 μM CTL Coenzyme Q10 (CoQ10)    -   3. 100 μM CTL Coenzyme Q10    -   4. 12.5 mM Lactic Acid    -   5. 12.5 mM Lactic Acid+50 μM CTL Coenzyme Q10    -   6. 12.5 mM Lactic Acid+100 μM CTL Coenzyme Q10    -   7. Hypoxia    -   8. Hypoxia+50 μM CTL Coenzyme Q10    -   9. Hypoxia+100 μM CTL Coenzyme Q10    -   10. Hypoxia+12.5 mM Lactic Acid    -   11. Hypoxia+12.5 mM Lactic Acid+50 μM CTL Coenzyme Q10    -   12. Hypoxia+12.5 mM Lactic Acid+100 μM CTL Coenzyme Q10    -   13. Media+22 mM Glucose    -   14. 50 μM CTL Coenzyme Q10+22 mM Glucose    -   15. 100 μM CTL Coenzyme Q10+22 mM Glucose    -   16. 12.5 mM Lactic Acid+22 mM Glucose    -   17. 12.5 mM Lactic Acid+22 mM Glucose+50 μM CTL Coenzyme Q10    -   18. 12.5 mM Lactic Acid+22 mM Glucose+100 μM CTL Coenzyme Q10    -   19. Hypoxia+22 mM Glucose    -   20. Hypoxia+22 mM Glucose+50 μM CTL Coenzyme Q10    -   21. Hypoxia+22 mM Glucose+100 μM CTL Coenzyme Q10    -   22. Hypoxia+12.5 mM Lactic Acid+22 mM Glucose    -   23. Hypoxia+12.5 mM Lactic Acid+22 mM Glucose+50 μM CTL Coenzyme        Q10    -   24. Hypoxia+12.5 mM Lactic Acid+22 mM Glucose+100 μM CTL        Coenzyme Q10

As a control one or more normal cell lines (e.g., THLE2 and HDFa) arecultured under similar conditions in order to identify cancer uniqueproteins or pathways (see below). The control may be the comparison cellmodel described above.

Multiple cancer cells of the same or different origin (for example,cancer lines PaCa2, HepG2, PC3 and MCF7), as opposed to a single cancercell type, may be included in the cancer model. In certain situations,cross talk or ECS experiments between different cancer cells (e.g.,HepG2 and PaCa2) may be conducted for several inter-related purposes.

In some embodiments that involve cross talk, experiments conducted onthe cell models are designed to determine modulation of cellular stateor function of one cell system or population (e.g., Hepatocarcinoma cellHepG2) by another cell system or population (e.g., Pancreatic cancerPaCa2) under defined treatment conditions (e.g., hyperglycemia, hypoxia(ischemia)). According to a typical setting, a first cellsystem/population is contacted by an external stimulus components, suchas a candidate molecule (e.g., a small drug molecule, a protein) or acandidate condition (e.g., hypoxia, high glucose environment). Inresponse, the first cell system/population changes its transcriptome,proteome, metabolome, and/or interactome, leading to changes that can bereadily detected both inside and outside the cell. For example, changesin transcriptome can be measured by the transcription level of aplurality of target mRNAs; changes in proteome can be measured by theexpression level of a plurality of target proteins; and changes inmetabolome can be measured by the level of a plurality of targetmetabolites by assays designed specifically for given metabolites.Alternatively, the above referenced changes in metabolome and/orproteome, at least with respect to certain secreted metabolites orproteins, can also be measured by their effects on the second cellsystem/population, including the modulation of the transcriptome,proteome, metabolome, and interactome of the second cellsystem/population. Therefore, the experiments can be used to identifythe effects of the molecule(s) of interest secreted by the first cellsystem/population on a second cell system/population under differenttreatment conditions. The experiments can also be used to identify anyproteins that are modulated as a result of signaling from the first cellsystem (in response to the external stimulus component treatment) toanother cell system, by, for example, differential screening ofproteomics. The same experimental setting can also be adapted for areverse setting, such that reciprocal effects between the two cellsystems can also be assessed. In general, for this type of experiment,the choice of cell line pairs is largely based on the factors such asorigin, disease state and cellular function.

Although two-cell systems are typically involved in this type ofexperimental setting, similar experiments can also be designed for morethan two cell systems by, for example, immobilizing each distinct cellsystem on a separate solid support.

Once the custom model is built, one or more “perturbations” may beapplied to the system, such as genetic variation from patient topatient, or with/without treatment by certain drugs or pro-drugs. SeeFIG. 15D. The effects of such perturbations to the system, including theeffect on disease related cancer cells, and disease related normalcontrol cells, can be measured using various art-recognized orproprietary means, as described in section III.B below.

In an exemplary experiment, cancer lines PaCa2, HepG2, PC3 and MCF7, andnormal cell lines THLE2 and HDFa, are conditioned in each ofhyperglycemia, hypoxia, and lactic acid-rich conditions, as well as inall combinations of two or three of three conditions, and in additionwith or without an environmental perturbation, specifically treatment byCoenzymeQ10.

The custom built cell model may be established and used throughout thesteps of the Platform Technology of the invention to ultimately identifya causal relationship unique in the biological system, by carrying outthe steps described herein. It will be understood by the skilledartisan, however, that a custom built cell model that is used togenerate an initial, “first generation” consensus causal relationshipnetwork for a biological process can continually evolve or expand overtime, e.g., by the introduction of additional cancer or normal celllines and/or additional cancer conditions. Additional data from theevolved cell model, i.e., data from the newly added portion(s) of thecell model, can be collected. The new data collected from an expanded orevolved cell model, i.e., from newly added portion(s) of the cell model,can then be introduced to the data sets previously used to generate the“first generation” consensus causal relationship network in order togenerate a more robust “second generation” consensus causal relationshipnetwork. New causal relationships unique to the biological system canthen be identified from the “second generation” consensus causalrelationship network. In this way, the evolution of the cell modelprovides an evolution of the consensus causal relationship networks,thereby providing new and/or more reliable insights into the modulatorsof the biological system.

Additional examples of custom built cell models are described in detailherein.

B. Data Collection

In general, two types of data may be collected from any custom builtmodel systems. One type of data (e.g., the first set of data, the thirdset of data) usually relates to the level of certain macromolecules,such as DNA, RNA, protein, lipid, etc. An exemplary data set in thiscategory is proteomic data (e.g., qualitative and quantitative dataconcerning the expression of all or substantially all measurableproteins from a sample). The other type of data is generally functionaldata (e.g., the second set of data, the fourth set of data) thatreflects the phenotypic changes resulting from the changes in the firsttype of data.

With respect to the first type of data, in some example embodiments,quantitative polymerase chain reaction (qPCR) and proteomics areperformed to profile changes in cellular mRNA and protein expression byquantitative polymerase chain reaction (qPCR) and proteomics. Total RNAcan be isolated using a commercial RNA isolation kit. Following cDNAsynthesis, specific commercially available qPCR arrays (e.g., those fromSA Biosciences) for disease area or cellular processes such asangiogenesis, apoptosis, and diabetes, may be employed to profile apredetermined set of genes by following a manufacturer's instructions.For example, the Biorad cfx-384 amplification system can be used for alltranscriptional profiling experiments. Following data collection (Ct),the final fold change over control can be determined using the δCtmethod as outlined in manufacturer's protocol. Proteomic sample analysiscan be performed as described in subsequent sections.

The subject method may employ large-scale high-throughput quantitativeproteomic analysis of hundreds of samples of similar character, andprovides the data necessary for identifying the cellular outputdifferentials.

There are numerous art-recognized technologies suitable for thispurpose. An exemplary technique, iTRAQ analysis in combination with massspectrometry, is briefly described below.

The quantitative proteomics approach is based on stable isotope labelingwith the 8-plex iTRAQ reagent and 2D-LC MALDI MS/MS for peptideidentification and quantification. Quantification with this technique isrelative: peptides and proteins are assigned abundance ratios relativeto a reference sample. Common reference samples in multiple iTRAQexperiments facilitate the comparison of samples across multiple iTRAQexperiments.

For example, to implement this analysis scheme, six primary samples andtwo control pool samples can be combined into one 8-plex iTRAQ mixaccording to the manufacturer's suggestions. This mixture of eightsamples then can be fractionated by two-dimensional liquidchromatography; strong cation exchange (SCX) in the first dimension, andreversed-phase HPLC in the second dimension, then can be subjected tomass spectrometric analysis.

A brief overview of exemplary laboratory procedures that can be employedis provided herein.

Protein extraction: Cells can be lysed with 8 M urea lysis buffer withprotease inhibitors (Thermo Scientific Halt Protease inhibitorEDTA-free) and incubate on ice for 30 minutes with vertex for 5 secondsevery 10 minutes. Lysis can be completed by ultrasonication in 5 secondspulse. Cell lysates can be centrifuged at 14000×g for 15 minutes (4° C.)to remove cellular debris. Bradford assay can be performed to determinethe protein concentration. 100 ug protein from each samples can bereduced (10 mM Dithiothreitol (DTT), 55° C., 1 h), alkylated (25 mMiodoacetamide, room temperature, 30 minutes) and digested with Trypsin(1:25 w/w, 200 mM triethylammonium bicarbonate (TEAB), 37° C., 16 h).

Secretome sample preparation: 1) In one embodiment, the cells can becultured in serum free medium: Conditioned media can be concentrated byfreeze dryer, reduced (10 mM Dithiothreitol (DTT), 55° C., 1 h),alkylated (25 mM iodoacetamide, at room temperature, incubate for 30minutes), and then desalted by actone precipitation. Equal amount ofproteins from the concentrated conditioned media can be digested withTrypsin (1:25 w/w, 200 mM triethylammonium bicarbonate (TEAB), 37° C.,16 h).

In one embodiment, the cells can be cultured in serum containing medium:The volume of the medium can be reduced using 3k MWCO Vivaspin columns(GE Healthcare Life Sciences), then can be reconstituted with 1×PBS(Invitrogen). Serum albumin can be depleted from all samples usingAlbuVoid column (Biotech Support Group, LLC) following themanufacturer's instructions with the modifications of buffer-exchange tooptimize for condition medium application.

iTRAQ 8 Plex Labeling: Aliquot from each tryptic digests in eachexperimental set can be pooled together to create the pooled controlsample. Equal aliquots from each sample and the pooled control samplecan be labeled by iTRAQ 8 Plex reagents according to the manufacturer'sprotocols (AB Sciex). The reactions can be combined, vacuumed todryness, re-suspended by adding 0.1% formic acid, and analyzed byLC-MS/MS.

2D-NanoLC-MS/MS: All labeled peptides mixtures can be separated byonline 2D-nanoLC and analysed by electrospray tandem mass spectrometry.The experiments can be carried out on an Eksigent 2D NanoLC Ultra systemconnected to an LTQ Orbitrap Velos mass spectrometer equipped with ananoelectrospray ion source (Thermo Electron, Bremen, Germany).

The peptides mixtures can be injected into a 5 cm SCX column (300 μm ID,5 μm, PolySULFOETHYL Aspartamide column from PolyLC, Columbia, Md.) witha flow of 4 μL/min and eluted in 10 ion exchange elution segments into aC18 trap column (2.5 cm, 100 μm ID, 5 μm, 300 Å ProteoPep II from NewObjective, Woburn, Mass.) and washed for 5 min with H2O/0.1% FA. Theseparation then can be further carried out at 300 mL/min using agradient of 2-45% B (H2O/0.1% FA (solvent A) and ACN/0.1% FA (solventB)) for 120 minutes on a 15 cm fused silica column (75 μm ID, 5 μm, 300Å ProteoPep II from New Objective, Woburn, Mass.).

Full scan MS spectra (m/z 300-2000) can be acquired in the Orbitrap withresolution of 30,000. The most intense ions (up to 10) can besequentially isolated for fragmentation using High energy C-trapDissociation (HCD) and dynamically exclude for 30 seconds. HCD can beconducted with an isolation width of 1.2 Da. The resulting fragment ionscan be scanned in the orbitrap with resolution of 7500. The LTQ OrbitrapVelos can be controlled by Xcalibur 2.1 with foundation 1.0.1.

Peptides/proteins identification and quantification: Peptides andproteins can be identified by automated database searching usingProteome Discoverer software (Thermo Electron) with Mascot search engineagainst SwissProt database. Search parameters can include 10 ppm for MStolerance, 0.02 Da for MS2 tolerance, and full trypsin digestionallowing for up to 2 missed cleavages. Carbamidomethylation (C) can beset as the fixed modification. Oxidation (M), TMT6, and deamidation (NQ)can be set as dynamic modifications. Peptides and proteinidentifications can be filtered with Mascot Significant Threshold(p<0.05). The filters can be allowed a 99% confidence level of proteinidentification (1% FDA).

The Proteome Discoverer software can apply correction factors on thereporter ions, and can reject all quantitation values if not allquantitation channels are present. Relative protein quantitation can beachieved by normalization at the mean intensity.

With respect to the second type of data, in some exemplary embodiments,bioenergetics profiling of cancer and normal models may employ theSeahorse™ XF24 analyzer to enable the understanding of glycolysis andoxidative phosphorylation components.

Specifically, cells can be plated on Seahorse culture plates at optimaldensities. These cells can be plated in 100 μl of media or treatment andleft in a 37° C. incubator with 5% CO₂. Two hours later, when the cellsare adhered to the 24 well plate, an additional 150 μl of either mediaor treatment solution can be added and the plates can be left in theculture incubator overnight. This two step seeding procedure allows foreven distribution of cells in the culture plate. Seahorse cartridgesthat contain the oxygen and pH sensor can be hydrated overnight in thecalibrating fluid in a non-CO₂ incubator at 37° C. Three mitochondrialdrugs are typically loaded onto three ports in the cartridge.Oligomycin, a complex III inhibitor, FCCP, an uncoupler and Rotenone, acomplex I inhibitor can be loaded into ports A, B and C respectively ofthe cartridge. All stock drugs can be prepared at a 10× concentration inan unbuffered DMEM media. The cartridges can be first incubated with themitochondrial compounds in a non-CO₂ incubator for about 15 minutesprior to the assay. Seahorse culture plates can be washed in DMEM basedunbuffered media that contains glucose at a concentration found in thenormal growth media. The cells can be layered with 630 ul of theunbuffered media and can be equilibriated in a non-CO₂ incubator beforeplacing in the Seahorse instrument with a precalibrated cartridge. Theinstrument can be run for three-four loops with a mix, wait and measurecycle for get a baseline, before injection of drugs through the port isinitiated. There can be two loops before the next drug is introduced.

OCR (Oxygen consumption rate) and ECAR (Extracullular AcidificationRate) can be recorded by the electrodes in a 7 μl chamber and can becreated with the cartridge pushing against the seahorse culture plate.

C. Data Integration and in Silico Model Generation

Once relevant data sets have been obtained, integration of data sets andgeneration of computer-implemented statistical models may be performedusing an AI-based informatics system or platform (e.g, the REFS™platform). For example, an exemplary AI-based system may producesimulation-based networks of protein associations as key drivers ofmetabolic end points (ECAR/OCR). See FIG. 15. Some background detailsregarding the REFS™ system may be found in Xing et al., “Causal ModelingUsing Network Ensemble Simulations of Genetic and Gene Expression DataPredicts Genes Involved in Rheumatoid Arthritis,” PLoS ComputationalBiology, vol. 7, issue. 3, 1-19 (March 2011) (e100105) and U.S. Pat. No.7,512,497 to Periwal, the entire contents of each of which is expresslyincorporated herein by reference in its entirety. In essence, asdescribed earlier, the REFS™ system is an AI-based system that employsmathematical algorithms to establish causal relationships among theinput variables (e.g., protein expression levels, mRNA expressionlevels, and the corresponding functional data, such as the OCR/ECARvalues measured on Seahorse culture plates). This process is based onlyon the input data alone, without taking into consideration priorexisting knowledge about any potential, established, and/or verifiedbiological relationships.

In particular, a significant advantage of the platform of the inventionis that the AI-based system is based on the data sets obtained from thecell model, without resorting to or taking into consideration anyexisting knowledge in the art concerning the biological process.Further, preferably, no data points are statistically or artificiallycut-off and, instead, all obtained data is fed into the AI-system fordetermining protein associations. Accordingly, the resulting statisticalmodels generated from the platform are unbiased, since they do not takeinto consideration any known biological relationships.

Specifically, data from the proteomics and ECAR/OCR can be input intothe AI-based information system, which builds statistical models basedon data associations, as described above. Simulation-based networks ofprotein associations are then derived for each disease versus normalscenario, including treatments and conditions using the followingmethods.

A detailed description of an exemplary process for building thegenerated (e.g., optimized or evolved) networks appears below withrespect to FIG. 16. As described above, data from the proteomics andfunctional cell data is input into the AI-based system (step 210). Theinput data, which may be raw data or minimally processed data, ispre-processed, which may include normalization (e.g., using a quantilefunction or internal standards) (step 212). The pre-processing may alsoinclude imputing missing data values (e.g., by using the K-nearestneighbor (K-NN) algorithm) (step 212).

The pre-processed data is used to construct a network fragment library(step 214). The network fragments define quantitative, continuousrelationships among all possible small sets (e.g., 2-3 member sets or2-4 member sets) of measured variables (input data). The relationshipsbetween the variables in a fragment may be linear, logistic,multinomial, dominant or recessive homozygous, etc. The relationship ineach fragment is assigned a Bayesian probabilistic score that reflecthow likely the candidate relationship is given the input data, and alsopenalizes the relationship for its mathematical complexity. By scoringall of the possible pairwise and three-way relationships (and in someembodiments also four-way relationships) inferred from the input data,the most likely fragments in the library can be identified (the likelyfragments). Quantitative parameters of the relationship are alsocomputed based on the input data and stored for each fragment. Variousmodel types may be used in fragment enumeration including but notlimited to linear regression, logistic regression, (Analysis ofVariance) ANOVA models, (Analysis of Covariance) ANCOVA models,non-linear/polynomial regression models and even non-parametricregression. The prior assumptions on model parameters may assume Gulldistributions or Bayesian Information Criterion (BIC) penalties relatedto the number of parameters used in the model. In a network inferenceprocess, each network in an ensemble of initial trial networks isconstructed from a subset of fragments in the fragment library. Eachinitial trial network in the ensemble of initial trial networks isconstructed with a different subset of the fragments from the fragmentlibrary (step 216).

An overview of the mathematical representations underlying the Bayesiannetworks and network fragments, which is based on Xing et al., “CausalModeling Using Network Ensemble Simulations of Genetic and GeneExpression Data Predicts Genes Involved in Rheumatoid Arthritis,” PLoSComputational Biology, vol. 7, issue. 3, 1-19 (March 2011) (e100105), ispresented below.

A multivariate system with random variables X=X₁, . . . , X_(n) may becharacterized by a multivariate probability distribution function P(X₁,. . . , X_(n); Θ), that includes a large number of parameters Θ. Themultivariate probability distribution function may be factorized andrepresented by a product of local conditional probability distributions:

${{P\left( {X_{1},\ldots\;,{X_{n};\Theta}} \right)} = {\prod\limits_{i - 1}^{n}\;{P_{i}\left( {{X_{i}\text{|}Y_{j\; 1}},\ldots\;,{Y_{{jK}_{i}};\Theta_{i}}} \right)}}},$in which each variable X_(i) is independent from its non-descendentvariables given its K_(i) parent variables, which are Y_(j1), . . . ,Y_(jK) _(i) . After factorization, each local probability distributionhas its own parameters Θ_(i).

The multivariate probability distribution function may be factorized indifferent ways with each particular factorization and correspondingparameters being a distinct probabilistic model. Each particularfactorization (model) can be represented by a Directed Acrylic Graph(DAC) having a vertex for each variable X_(i) and directed edges betweenvertices representing dependences between variables in the localconditional distributions P_(i)(X_(i)|Y_(j1), . . . , Y_(jK) _(i) ).Subgraphs of a DAG, each including a vertex and associated directededges are network fragments.

A model is evolved or optimized by determining the most likelyfactorization and the most likely parameters given the input data. Thismay be described as “learning a Bayesian network,” or, in other words,given a training set of input data, finding a network that best matchesthe input data. This is accomplished by using a scoring function thatevaluates each network with respect to the input data.

A Bayesian framework is used to determine the likelihood of afactorization given the input data. Bayes Law states that the posteriorprobability, P(D|M), of a model M, given data D is proportional to theproduct of the product of the posterior probability of the data giventhe model assumptions, P(D|M), multiplied by the prior probability ofthe model, P(M), assuming that the probability of the data, P(D), isconstant across models. This is expressed in the following equation:

${P\left( {M\text{|}D} \right)} = {\frac{{P\left( {D\text{|}M} \right)}*{P(M)}}{P(D)}.}$The posterior probability of the data assuming the model is the integralof the data likelihood over the prior distribution of parameters:P(D|M)=∫P(D|M(Θ))P(Θ|M)dΘAssuming all models are equally likely (i.e., that P(M) is a constant),the posterior probability of model M given the data D may be factoredinto the product of integrals over parameters for each local networkfragment M_(i) as follows:

${P\left( {M\text{|}D} \right)} = {\prod\limits_{i = 1}^{n}{\int\;{{P_{i}\left( {{X_{i}\text{|}Y_{j\; 1}},\ldots\;,{Y_{{jK}_{i}};\Theta_{i}}} \right)}.}}}$Note that in the equation above, a leading constant term has beenomitted. In some embodiments, a Bayesian Information Criterion (BIC),which takes a negative logarithm of the posterior probability of themodel P(D|M) may be used to “Score” each model as follows:

${{S_{tot}(M)} = {{{- \log}\;{P\left( {M\text{|}D} \right)}} = {\sum\limits_{i = 1}^{n}{S\left( M_{i} \right)}}}},$where the total score S_(tot) for a model M is a sum of the local scoresS_(i) for each local network fragment. The BIC further gives anexpression for determining a score each individual network fragment:

${{S\left( M_{i} \right)} \approx {S_{BIC}\left( M_{i} \right)}} = {{S_{MLE}\left( M_{i} \right)} + {\frac{\kappa\left( M_{i} \right)}{2}\log\; N}}$where κ(M_(i)) is the number of fitting parameter in model M_(i) and Nis the number of samples (data points). S_(MLE)(M_(i)) is the negativelogarithm of the likelihood function for a network fragment, which maybe calculated from the functional relationships used for each networkfragment. For a BIC score, the lower the score, the more likely a modelfits the input data.

The ensemble of trial networks is globally optimized, which may bedescribed as optimizing or evolving the networks (step 218). Forexample, the trial networks may be evolved and optimized according to aMetropolis Monte Carlo Sampling algorithm. Simulated annealing may beused to optimize or evolve each trial network in the ensemble throughlocal transformations. In an example simulated annealing processes, eachtrial network is changed by adding a network fragment from the library,by deleted a network fragment from the trial network, by substituting anetwork fragment or by otherwise changing network topology, and then anew score for the network is calculated. Generally speaking, if thescore improves, the change is kept and if the score worsens the changeis rejected. A “temperature” parameter allows some local changes whichworsen the score to be kept, which aids the optimization process inavoiding some local minima. The “temperature” parameter is decreasedover time to allow the optimization/evolution process to converge.

All or part of the network inference process may be conducted inparallel for the trial different networks. Each network may be optimizedin parallel on a separate processor and/or on a separate computingdevice. In some embodiments, the optimization process may be conductedon a supercomputer incorporating hundreds to thousands of processorswhich operate in parallel. Information may be shared among theoptimization processes conducted on parallel processors.

The optimization process may include a network filter that drops anynetworks from the ensemble that fail to meet a threshold standard foroverall score. The dropped network may be replaced by a new initialnetwork. Further any networks that are not “scale free” may be droppedfrom the ensemble. After the ensemble of networks has been optimized orevolved, the result may be termed an ensemble of generated cell modelnetworks, which may be collectively referred to as the generatedconsensus network.

D. Simulation to Extract Quantitative Relationship Information and forPrediction

Simulation may be used to extract quantitative parameter informationregarding each relationship in the generated cell model networks (step220). For example, the simulation for quantitative informationextraction may involve perturbing (increasing or decreasing) each nodein the network by 10 fold and calculating the posterior distributionsfor the other nodes (e.g., proteins) in the models. The endpoints arecompared by t-test with the assumption of 100 samples per group and the0.01 significance cut-off. The t-test statistic is the median of 100t-tests. Through use of this simulation technique, an AUC (area underthe curve) representing the strength of prediction and fold changerepresenting the in silico magnitude of a node driving an end point aregenerated for each relationship in the ensemble of networks.

A relationship quantification module of a local computer system may beemployed to direct the AI-based system to perform the perturbations andto extract the AUC information and fold information. The extractedquantitative information may include fold change and AUC for each edgeconnecting a parent note to a child node. In some embodiments, acustom-built R program may be used to extract the quantitativeinformation.

In some embodiments, the ensemble of generated cell model networks canbe used through simulation to predict responses to changes inconditions, which may be later verified though wet-lab cell-based, oranimal-based, experiments.

The output of the AI-based system may be quantitative relationshipparameters and/or other simulation predictions (222).

E. Generation of Differential (Delta) Networks

A differential network creation module may be used to generatedifferential (delta) networks between generated cell model networks andgenerated comparison cell model networks. As described above, in someembodiments, the differential network compares all of the quantitativeparameters of the relationships in the generated cell model networks andthe generated comparison cell model network. The quantitative parametersfor each relationship in the differential network are based on thecomparison. In some embodiments, a differential may be performed betweenvarious differential networks, which may be termed a delta-deltanetwork. An example of a delta-delta network is described below withrespect to FIG. 26 in the Examples section. The differential networkcreation module may be a program or script written in PERL.

F. Visualization of Networks

The relationship values for the ensemble of networks and for thedifferential networks may be visualized using a network visualizationprogram (e.g., Cytoscape open source platform for complex networkanalysis and visualization from the Cytoscape consortium). In the visualdepictions of the networks, the thickness of each edge (e.g., each lineconnecting the proteins) represents the strength of fold change. Theedges are also directional indicating causality, and each edge has anassociated prediction confidence level.

G. Exemplary Computer System

FIG. 17 schematically depicts an exemplary computer system/environmentthat may be employed in some embodiments for communicating with theAI-based informatics system, for generating differential networks, forvisualizing networks, for saving and storing data, and/or forinteracting with a user. As explained above, calculations for anAI-based informatics system may be performed on a separate supercomputerwith hundreds or thousands of parallel processors that interacts,directly or indirectly, with the exemplary computer system. Theenvironment includes a computing device 100 with associated peripheraldevices. Computing device 100 is programmable to implement executablecode 150 for performing various methods, or portions of methods, taughtherein. Computing device 100 includes a storage device 116, such as ahard-drive, CD-ROM, or other non-transitory computer readable media.Storage device 116 may store an operating system 118 and other relatedsoftware. Computing device 100 may further include memory 106. Memory106 may comprise a computer system memory or random access memory, suchas DRAM, SRAM, EDO RAM, etc. Memory 106 may comprise other types ofmemory as well, or combinations thereof. Computing device 100 may store,in storage device 116 and/or memory 106, instructions for implementingand processing each portion of the executable code 150.

The executable code 150 may include code for communicating with theAI-based informatics system 190, for generating differential networks(e.g., a differential network creation module), for extractingquantitative relationship information from the AI-based informaticssystem (e.g., a relationship quantification module) and for visualizingnetworks (e.g., Cytoscape).

In some embodiments, the computing device 100 may communicate directlyor indirectly with the AI-based informatics system 190 (e.g., a systemfor executing REFS). For example, the computing device 100 maycommunicate with the AI-based informatics system 190 by transferringdata files (e.g., data frames) to the AI-based informatics system 190through a network. Further, the computing device 100 may have executablecode 150 that provides an interface and instructions to the AI-basedinformatics system 190.

In some embodiments, the computing device 100 may communicate directlyor indirectly with one or more experimental systems 180 that providedata for the input data set. Experimental systems 180 for generatingdata may include systems for mass spectrometry based proteomics,microarray gene expression, qPCR gene expression, mass spectrometrybased metabolomics, and mass spectrometry based lipidomics, SNPmicroarrays, a panel of functional assays, and other in-vitro biologyplatforms and technologies.

Computing device 100 also includes processor 102, and may include one ormore additional processor(s) 102′, for executing software stored in thememory 106 and other programs for controlling system hardware,peripheral devices and/or peripheral hardware. Processor 102 andprocessor(s) 102′ each can be a single core processor or multiple core(104 and 104′) processor. Virtualization may be employed in computingdevice 100 so that infrastructure and resources in the computing devicecan be shared dynamically. Virtualized processors may also be used withexecutable code 150 and other software in storage device 116. A virtualmachine 114 may be provided to handle a process running on multipleprocessors so that the process appears to be using only one computingresource rather than multiple. Multiple virtual machines can also beused with one processor.

A user may interact with computing device 100 through a visual displaydevice 122, such as a computer monitor, which may display a userinterface 124 or any other interface. The user interface 124 of thedisplay device 122 may be used to display raw data, visualrepresentations of networks, etc. The visual display device 122 may alsodisplay other aspects or elements of exemplary embodiments (e.g., anicon for storage device 116). Computing device 100 may include other I/Odevices such a keyboard or a multi-point touch interface (e.g., atouchscreen) 108 and a pointing device 110, (e.g., a mouse, trackballand/or trackpad) for receiving input from a user. The keyboard 108 andthe pointing device 110 may be connected to the visual display device122 and/or to the computing device 100 via a wired and/or a wirelessconnection.

Computing device 100 may include a network interface 112 to interfacewith a network device 126 via a Local Area Network (LAN), Wide AreaNetwork (WAN) or the Internet through a variety of connectionsincluding, but not limited to, standard telephone lines, LAN or WANlinks (e.g., 802.11, T1, T3, 56 kb, X.25), broadband connections (e.g.,ISDN, Frame Relay, ATM), wireless connections, controller area network(CAN), or some combination of any or all of the above. The networkinterface 112 may comprise a built-in network adapter, network interfacecard, PCMCIA network card, card bus network adapter, wireless networkadapter, USB network adapter, modem or any other device suitable forenabling computing device 100 to interface with any type of networkcapable of communication and performing the operations described herein.

Moreover, computing device 100 may be any computer system such as aworkstation, desktop computer, server, laptop, handheld computer orother form of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein.

Computing device 100 can be running any operating system 118 such as anyof the versions of the MICROSOFT WINDOWS operating systems, thedifferent releases of the Unix and Linux operating systems, any versionof the MACOS for Macintosh computers, any embedded operating system, anyreal-time operating system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device and performing the operations described herein. Theoperating system may be running in native mode or emulated mode.

IV. Models for a Biological System and Uses Therefor

A. Establishing a Model for a Biological System

Virtually all biological systems or processes involve complicatedinteractions among different cell types and/or organ systems.Perturbation of critical functions in one cell type or organ may lead tosecondary effects on other interacting cells types and organs, and suchdownstream changes may in turn feedback to the initial changes and causefurther complications. Therefore, it is beneficial to dissect a givenbiological system or process to its components, such as interactionbetween pairs of cell types or organs, and systemically probe theinteractions between these components in order to gain a more complete,global view of the biological system or process.

Accordingly, the present invention provides cell models for biologicalsystems. To this end, Applicants have built cell models for severalexemplary biological systems which have been employed in the subjectdiscovery Platform Technology. Applicants have conducted experimentswith the cell models using the subject discovery Platform Technology togenerate consensus causal relationship networks, including causalrelationships unique in the biological system, and thereby identify“modulators” or critical molecular “drivers” important for theparticular biological systems or processes.

One significant advantage of the Platform Technology and its components,e.g., the custom built cell models and data sets obtained from the cellmodels, is that an initial, “first generation” consensus causalrelationship network generated for a biological system or process cancontinually evolve or expand over time, e.g., by the introduction ofadditional cell lines/types and/or additional conditions. Additionaldata from the evolved cell model, i.e., data from the newly addedportion(s) of the cell model, can be collected. The new data collectedfrom an expanded or evolved cell model, i.e., from newly addedportion(s) of the cell model, can then be introduced to the data setspreviously used to generate the “first generation” consensus causalrelationship network in order to generate a more robust “secondgeneration” consensus causal relationship network. New causalrelationships unique to the biological system can then be identifiedfrom the “second generation” consensus causal relationship network. Inthis way, the evolution of the cell model provides an evolution of theconsensus causal relationship networks, thereby providing new and/ormore reliable insights into the modulators of the biological system. Inthis way, both the cell models, the data sets from the cell models, andthe causal relationship networks generated from the cell models by usingthe Platform Technology methods can constantly evolve and build uponprevious knowledge obtained from the Platform Technology.

Accordingly, the invention provides consensus causal relationshipnetworks generated from the cell models employed in the PlatformTechnology. These consensus causal relationship networks may be firstgeneration consensus causal relationship networks, or may be multiplegeneration consensus causal relationship networks, e.g., 2^(nd), 3^(rd),4^(th), 5^(th), 6^(th), 7^(th), 8^(th), 9^(th), 10^(th), 11^(th),12^(th), 13^(th), 14^(th), 15^(th), 16^(th), 17^(th), 18^(th), 19^(th),20^(th) or greater generation consensus causal relationship networks.Further, the invention provides simulated consensus causal relationshipnetworks generated from the cell models employed in the PlatformTechnology. These simulated consensus causal relationship networks maybe first generation simulated consensus causal relationship networks, ormay be multiple generation simulated consensus causal relationshipnetworks, e.g., 2^(nd), 3^(rd), 4^(th), 5^(th), 6^(th), 7^(th), 8^(th),9^(th), 10^(th), 11^(th), 12^(th), 13^(th), 14^(th), 15^(th), 16^(th),17^(th), 18^(th), 19^(th), 20^(th) or greater simulated generationconsensus causal relationship networks. The invention further providesdelta networks and delta-delta networks generated from any of theconsensus causal relationship networks of the invention.

A custom built cell model for a biological system or process comprisesone or more cells associated with the biological system. The model for abiological system/process may be established to simulate an environmentof biological system, e.g., environment of a cancer cell in vivo, bycreating conditions (e.g., cell culture conditions) that mimic acharacteristic aspect of the biological system or process.

Multiple cells of the same or different origin, as opposed to a singlecell type, may be included in the cell model. In one embodiment, atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 50 or more different cell lines or cell types areincluded in the cell model. In one embodiment, the cells are all of thesame type, e.g., all breast cancer cells or plant cells, but aredifferent established cell lines, e.g., different established cell linesof breast cancer cells or plant cells. All values presented in theforegoing list can also be the upper or lower limit of ranges, that areintended to be a part of this invention, e.g., between 1 and 5, 1 and10, 2 and 5, or 5 and 15 different cell lines or cell types.

Examples of cell types that may be included in the cell models of theinvention include, without limitation, human cells, animal cells,mammalian cells, plant cells, yeast, bacteria, or fungae. In oneembodiment, cells of the cell model can include diseased cells, such ascancer cells or bacterially or virally infected cells. In oneembodiment, cells of the cell model can include disease-associatedcells, such as cells involved in diabetes, obesity or cardiovasculardisease state, e.g., aortic smooth muscle cells or hepatocytes. Theskilled person would recognize those cells that are involved in orassociated with a particular biological state/process, e.g., diseasestate/process, and any such cells may be included in a cell model of theinvention.

Cell models of the invention may include one or more “control cells.” Inone embodiment, a control cell may be an untreated or unperturbed cell.In another embodiment, a “control cell” may be a normal, e.g.,non-diseased, cell. In one embodiment, at least 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 ormore different control cells are included in the cell model. All valuespresented in the foregoing list can also be the upper or lower limit ofranges, that are intended to be a part of this invention, e.g., between1 and 5, 1 and 10, 2 and 5, or 5 and 15 different control cell lines orcontrol cell types. In one embodiment, the control cells are all of thesame type but are different established cell lines of that cell type. Inone embodiment, as a control, one or more normal, e.g., non-diseased,cell lines are cultured under similar conditions, and/or are exposed tothe same perturbation, as the primary cells of the cell model in orderto identify proteins or pathways unique to the biological state orprocess.

A custom cell model of the invention may also comprise conditions thatmimic a characteristic aspect of the biological state or process. Forexample, cell culture conditions may be selected that closelyapproximating the conditions of a cancer cell in a tumor environment invivo, or of an aortic smooth muscle cell of a patient suffering fromcardiovascular disease. In some instances, the conditions are stressconditions. Various conditions/stressors may be employed in the cellmodels of the invention. In one embodiment, these stressors/conditionsmay constitute the “perturbation”, e.g., external stimulus, for the cellsystems. One exemplary stress condition is hypoxia, a conditiontypically found, for example, within solid tumors. Hypoxia can beinduced using art-recognized methods. For example, hypoxia can beinduced by placing cell systems in a Modular Incubator Chamber (MIC-101,Billups-Rothenberg Inc. Del Mar, Calif.), which can be flooded with anindustrial gas mix containing 5% CO₂, 2% O₂ and 93% nitrogen. Effectscan be measured after a pre-determined period, e.g., at 24 hours afterhypoxia treatment, with and without additional external stimuluscomponents (e.g., CoQ10 at 0, 50, or 100 μM). Likewise, lactic acidtreatment mimics a cellular environment where glycolysis activity ishigh. Lactic acid induced stress can be investigated at a final lacticacid concentration of about 12.5 mM at a pre-determined time, e.g., at24 hours, with or without additional external stimulus components (e.g.,CoQ10 at 0, 50, or 100 μM). Hyperglycemia is a condition found indiabetes as well as in cancer. A typical hyperglycemic condition thatcan be used to treat the subject cells include 10% culture grade glucoseadded to suitable media to bring up the final concentration of glucosein the media to about 22 mM. Hyperlipidemia is a condition found, forexample, in obesity and cardiovascular disease. The hyperlipidemicconditions can be provided by culturing cells in media containing 0.15mM sodium palmitate. Hyperinsulinemia is a condition found, for example,in diabetes. The hyperinsulinemic conditions may be induced by culturingthe cells in media containing 1000 nM insulin.

Individual conditions may be investigated separately in the custom builtcell models of the invention, and/or may be combined together. In oneembodiment, a combination of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,20, 25, 30, 40, 50 or more conditions reflecting or simulating differentcharacteristic aspects of the biological system are investigated in thecustom built cell model. In one embodiment, individual conditions and,in addition, combinations of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50 or more of the conditionsreflecting or simulating different characteristic aspects of thebiological system are investigated in the custom built cell model. Allvalues presented in the foregoing list can also be the upper or lowerlimit of ranges, that are intended to be a part of this invention, e.g.,between 1 and 5, 1 and 10, 1 and 20, 1 and 30, 2 and 5, 2 and 10, 5 and10, 1 and 20, 5 and 20, 10 and 20, 10 and 25, 10 and 30 or 10 and 50different conditions.

Once the custom cell model is built, one or more “perturbations” may beapplied to the system, such as genetic variation from patient topatient, or with/without treatment by certain drugs or pro-drugs. SeeFIG. 15D. The effects of such perturbations to the cell model system canbe measured using various art-recognized or proprietary means, asdescribed in section III.B below.

The custom built cell model may be exposed to a perturbation, e.g., an“environmental perturbation” or “external stimulus component”. The“environmental perturbation” or “external stimulus component” may beendogenous to the cellular environment (e.g., the cellular environmentcontains some levels of the stimulant, and more of the same is added toincrease its level), or may be exogenous to the cellular environment(e.g., the stimulant/perturbation is largely absent from the cellularenvironment prior to the alteration). The cellular environment mayfurther be altered by secondary changes resulting from adding theenvironmental perturbation or external stimulus component, since theexternal stimulus component may change the cellular output of the cellsystem, including molecules secreted into the cellular environment bythe cell system. The environmental perturbation or external stimuluscomponent may include any external physical and/or chemical stimulusthat may affect cellular function. This may include any large or smallorganic or inorganic molecules, natural or synthetic chemicals,temperature shift, pH change, radiation, light (UVA, UVB etc.),microwave, sonic wave, electrical current, modulated or unmodulatedmagnetic fields, etc. The environmental perturbation or externalstimulus component may also include an introduced genetic modificationor mutation or a vehicle (e.g., vector) that causes a geneticmodification/mutation.

(i) Cross-Talk Cell Systems

In certain situations, where interaction between two or more cellsystems are desired to be investigated, a “cross-talking cell system”may be formed by, for example, bringing the modified cellularenvironment of a first cell system into contact with a second cellsystem to affect the cellular output of the second cell system.

As used herein, “cross-talk cell system” comprises two or more cellsystems, in which the cellular environment of at least one cell systemcomes into contact with a second cell system, such that at least onecellular output in the second cell system is changed or affected. Incertain embodiments, the cell systems within the cross-talk cell systemmay be in direct contact with one another. In other embodiments, none ofthe cell systems are in direct contact with one another.

For example, in certain embodiments, the cross-talk cell system may bein the form of a transwell, in which a first cell system is growing inan insert and a second cell system is growing in a corresponding wellcompartment. The two cell systems may be in contact with the same ordifferent media, and may exchange some or all of the media components.External stimulus component added to one cell system may besubstantially absorbed by one cell system and/or degraded before it hasa chance to diffuse to the other cell system. Alternatively, theexternal stimulus component may eventually approach or reach anequilibrium within the two cell systems.

In certain embodiments, the cross-talk cell system may adopt the form ofseparately cultured cell systems, where each cell system may have itsown medium and/or culture conditions (temperature, CO₂ content, pH,etc.), or similar or identical culture conditions. The two cell systemsmay come into contact by, for example, taking the conditioned mediumfrom one cell system and bringing it into contact with another cellsystem. Direct cell-cell contacts between the two cell systems can alsobe effected if desired. For example, the cells of the two cell systemsmay be co-cultured at any point if desired, and the co-cultured cellsystems can later be separated by, for example, FACS sorting when cellsin at least one cell system have a sortable marker or label (such as astably expressed fluorescent marker protein GFP).

Similarly, in certain embodiments, the cross-talk cell system may simplybe a co-culture. Selective treatment of cells in one cell system can beeffected by first treating the cells in that cell system, beforeculturing the treated cells in co-culture with cells in another cellsystem. The co-culture cross-talk cell system setting may be helpfulwhen it is desired to study, for example, effects on a second cellsystem caused by cell surface changes in a first cell system, afterstimulation of the first cell system by an external stimulus component.

The cross-talk cell system of the invention is particularly suitable forexploring the effect of certain pre-determined external stimuluscomponent on the cellular output of one or both cell systems. Theprimary effect of such a stimulus on the first cell system (with whichthe stimulus directly contact) may be determined by comparing cellularoutputs (e.g., protein expression level) before and after the first cellsystem's contact with the external stimulus, which, as used herein, maybe referred to as “(significant) cellular output differentials.” Thesecondary effect of such a stimulus on the second cell system, which ismediated through the modified cellular environment of the first cellsystem (such as its secretome), can also be similarly measured. There, acomparison in, for example, proteome of the second cell system can bemade between the proteome of the second cell system with the externalstimulus treatment on the first cell system, and the proteome of thesecond cell system without the external stimulus treatment on the firstcell system. Any significant changes observed (in proteome or any othercellular outputs of interest) may be referred to as a “significantcellular cross-talk differential.”

In making cellular output measurements (such as protein expression),either absolute expression amount or relative expression level may beused. For example, to determine the relative protein expression level ofa second cell system, the amount of any given protein in the second cellsystem, with or without the external stimulus to the first cell system,may be compared to a suitable control cell line and mixture of celllines and given a fold-increase or fold-decrease value. A pre-determinedthreshold level for such fold-increase (e.g., at least 1.2, 1.3, 1.4,1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 15,20, 25, 30, 35, 40, 45, 50, 75 or 100 or more fold increase) orfold-decrease (e.g., at least a decrease to 0.95, 0.9, 0.8, 0.75, 0.7,0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1 or 0.05 fold, or90%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%,15%, 10% or 5% or less) may be used to select significant cellularcross-talk differentials. All values presented in the foregoing list canalso be the upper or lower limit of ranges, e.g., between 1.5 and 5fold, between 2 and 10 fold, between 1 and 2 fold, or between 0.9 and0.7 fold, that are intended to be a part of this invention.

Throughout the present application, all values presented in a list,e.g., such as those above, can also be the upper or lower limit ofranges that are intended to be a part of this invention.

To illustrate, in one exemplary two-cell system established to imitateaspects of a cardiovascular disease model, a heart smooth muscle cellline (first cell system) may be treated with a hypoxia condition (anexternal stimulus component), and proteome changes in a kidney cell line(second cell system) resulting from contacting the kidney cells withconditioned medium of the heart smooth muscle may be measured usingconventional quantitative mass spectrometry. Significant cellularcross-talking differentials in these kidney cells may be determined,based on comparison with a proper control (e.g., similarly culturedkidney cells contacted with conditioned medium from similarly culturedheart smooth muscle cells not treated with hypoxia conditions).

Not every observed significant cellular cross-talking differentials maybe of biological significance. With respect to any given biologicalsystem for which the subject interrogative biological assessment isapplied, some (or maybe all) of the significant cellular cross-talkingdifferentials may be “determinative” with respect to the specificbiological problem at issue, e.g., either responsible for causing adisease condition (a potential target for therapeutic intervention) oris a biomarker for the disease condition (a potential diagnostic orprognostic factor).

Such determinative cross-talking differentials may be selected by an enduser of the subject method, or it may be selected by a bioinformaticssoftware program, such as DAVID-enabled comparative pathway analysisprogram, or the KEGG pathway analysis program. In certain embodiments,more than one bioinformatics software program is used, and consensusresults from two or more bioinformatics software programs are preferred.

As used herein, “differentials” of cellular outputs include differences(e.g., increased or decreased levels) in any one or more parameters ofthe cellular outputs. For example, in terms of protein expression level,differentials between two cellular outputs, such as the outputsassociated with a cell system before and after the treatment by anexternal stimulus component, can be measured and quantitated by usingart-recognized technologies, such as mass-spectrometry based assays(e.g., iTRAQ, 2D-LC-MSMS, etc.).

(ii) Cancer Specific Models

An example of a biological system or process is cancer. As any othercomplicated biological process or system, cancer is a complicatedpathological condition characterized by multiple unique aspects. Forexample, due to its high growth rate, many cancer cells are adapted togrow in hypoxia conditions, have up-regulated glycolysis and reducedoxidative phosphorylation metabolic pathways. As a result, cancer cellsmay react differently to an environmental perturbation, such astreatment by a potential drug, as compared to the reaction by a normalcell in response to the same treatment. Thus, it would be of interest todecipher cancer's unique responses to drug treatment as compared to theresponses of normal cells. To this end, a custom cancer model may beestablished to simulate the environment of a cancer cell, e.g., within atumor in vivo, by choosing appropriate cancer cell lines and creatingcell culture conditions that mimic a characteristic aspect of thedisease state or process. For example, cell culture conditions may beselected that closely approximating the conditions of a cancer cell in atumor in vivo, or to mimic various aspects of cancer growth, byisolating different growth conditions of the cancer cells.

Multiple cancer cells of the same or different origin (for example,cancer lines PaCa2, HepG2, PC3 and MCF7), as opposed to a single cancercell type, may be included in the cancer model. In one embodiment, atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 45, 50 or more different cancer cell lines or cancercell types are included in the cancer model. All values presented in theforegoing list can also be the upper or lower limit of ranges, that areintended to be a part of this invention, e.g., between 1 and 5, 1 and10, 2 and 5, or 5 and 15 different cancer cell lines or cell types.

In one embodiment, the cancer cells are all of the same type, e.g., allbreast cancer cells, but are different established cell lines, e.g.,different established cell lines of breast cancer.

Examples of cancer cell types that may be included in the cancer modelinclude, without limitation, lung cancer, breast cancer, prostatecancer, melanoma, squamous cell carcinoma, colorectal cancer, pancreaticcancer, thyroid cancer, endometrial cancer, bladder cancer, kidneycancer, solid tumor, leukemia, non-Hodgkin lymphoma. In one embodiment,a drug-resistant cancer cell may be included in the cancer model.Specific examples of cell lines that may be included in a cancer modelinclude, without limitation, PaCa2, HepG2, PC3 and MCF7 cells. Numerouscancer cell lines are known in the art, and any such cancer cell linemay be included in a cancer model of the invention.

Cell models of the invention may include one or more “control cells.” Inone embodiment, a control cell may be an untreated or unperturbed cancercell. In another embodiment, a “control cell” may be a normal,non-cancerous cell. Any one of numerous normal, non-cancerous cell linesmay be included in the cell model. In one embodiment, the normal cellsare one or more of THLE2 and HDFa cells. In one embodiment, at least 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25,30, 35, 40, 45, 50 or more different normal cell types are included inthe cancer model. All values presented in the foregoing list can also bethe upper or lower limit of ranges, that are intended to be a part ofthis invention, e.g., between 1 and 5, 1 and 10, 2 and 5, or 5 and 15different normal cell lines or cell types. In one embodiment, the normalcells are all of the same type, e.g., all healthy epithelial or breastcells, but are different established cell lines, e.g., differentestablished cell lines of epithelial or breast cells. In one embodiment,as a control, one or more normal non-cancerous cell lines (e.g., THLE2and HDFa) are cultured under similar conditions, and/or are exposed tothe same perturbation, as the cancer cells of the cell model in order toidentify cancer unique proteins or pathways.

A custom cancer model may also comprise cell culture conditions thatmimic a characteristic aspect of the cancerous state or process. Forexample, cell culture conditions may be selected that closelyapproximating the conditions of a cancer cell in a tumor environment invivo, or to mimic various aspects of cancer growth, by isolatingdifferent growth conditions of the cancer cells. In some instances thecell culture conditions are stress conditions.

One such cancer “environment”, or stress condition, is hypoxia, acondition typically found within a solid tumor. Hypoxia can be inducedin cells in cells using art-recognized methods. For example, hypoxia canbe induced by placing cell systems in a Modular Incubator Chamber(MIC-101, Billups-Rothenberg Inc. Del Mar, Calif.), which can be floodedwith an industrial gas mix containing 5% CO₂, 2% O₂ and 93% nitrogen.Effects can be measured after a pre-determined period, e.g., at 24 hoursafter hypoxia treatment, with and without additional external stimuluscomponents (e.g., CoQ10 at 0, 50, or 100 μM).

Likewise, lactic acid treatment of cells mimics a cellular environmentwhere glycolysis activity is high, as exists in the tumor environment invivo. Lactic acid induced stress can be investigated at a final lacticacid concentration of about 12.5 mM at a pre-determined time, e.g., at24 hours, with or without additional external stimulus components (e.g.,CoQ10 at 0, 50, or 100 μM).

Hyperglycemia is normally a condition found in diabetes; however,hyperglycemia also to some extent mimics one aspect of cancer growthbecause many cancer cells rely on glucose as their primary source ofenergy. Exposing subject cells to a typical hyperglycemic condition mayinclude adding 10% culture grade glucose to suitable media, such thatthe final concentration of glucose in the media is about 22 mM.

Individual conditions reflecting different aspects of cancer growth maybe investigated separately in the custom built cancer model, and/or maybe combined together. In one embodiment, combinations of at least 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more conditionsreflecting or simulating different aspects of cancer growth/conditionsare investigated in the custom built cancer model. In one embodiment,individual conditions and, in addition, combinations of at least 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more of the conditionsreflecting or simulating different aspects of cancer growth/conditionsare investigated in the custom built cancer model. All values presentedin the foregoing list can also be the upper or lower limit of ranges,that are intended to be a part of this invention, e.g., between 1 and 5,1 and 10, 1 and 20, 1 and 30, 2 and 5, 2 and 10, 5 and 10, 1 and 20, 5and 20, and 20, 10 and 25, 10 and 30 or 10 and 50 different conditions.

Once the custom cell model is built, one or more “perturbations” may beapplied to the system, such as genetic variation from patient topatient, or with/without treatment by certain drugs or pro-drugs. SeeFIG. 15D. The effects of such perturbations to the system, including theeffect on disease related cancer cells, and disease related normalcontrol cells, can be measured using various art-recognized orproprietary means, as described in section III.B below.

In an exemplary experiment, cancer lines PaCa2, HepG2, PC3 and MCF7, andnormal cell lines THLE2 and HDFa, are conditioned in each ofhyperglycemia, hypoxia, and lactic acid-rich conditions, as well as inall combinations of two or three of three conditions, and in additionwith or without an environmental perturbation, specifically treatment byCoenzyme Q10. Listed herein below are such exemplary combinations ofconditions, with or without a perturbation, Coenzyme Q10 treatment, thatcan be used to treat the cancer cells and/or control (e.g., normal)cells of the cancer cell model. Other combinations can be readilyformulated depending on the specific interrogative biological assessmentthat is being conducted.

-   -   1. Media only    -   2. 50 μM CTL Coenzyme Q10    -   3. 100 μM CTL Coenzyme Q10    -   4. 12.5 mM Lactic Acid    -   5. 12.5 mM Lactic Acid+50 μM CTL Coenzyme Q10    -   6. 12.5 mM Lactic Acid+100 μM CTL Coenzyme Q10    -   7. Hypoxia    -   8. Hypoxia+50 μM CTL Coenzyme Q10    -   9. Hypoxia+100 μM CTL Coenzyme Q10    -   10. Hypoxia+12.5 mM Lactic Acid    -   11. Hypoxia+12.5 mM Lactic Acid+50 μM CTL Coenzyme Q10    -   12. Hypoxia+12.5 mM Lactic Acid+100 μM CTL Coenzyme Q10    -   13. Media+22 mM Glucose    -   14. 50 μM CTL Coenzyme Q10+22 mM Glucose    -   15. 100 μM CTL Coenzyme Q10+22 mM Glucose    -   16. 12.5 mM Lactic Acid+22 mM Glucose    -   17. 12.5 mM Lactic Acid+22 mM Glucose+50 μM CTL Coenzyme Q10    -   18. 12.5 mM Lactic Acid+22 mM Glucose+100 μM CTL Coenzyme Q10    -   19. Hypoxia+22 mM Glucose    -   20. Hypoxia+22 mM Glucose+50 μM CTL Coenzyme Q10    -   21. Hypoxia+22 mM Glucose+100 μM CTL Coenzyme Q10    -   22. Hypoxia+12.5 mM Lactic Acid+22 mM Glucose    -   23. Hypoxia+12.5 mM Lactic Acid+22 mM Glucose+50 μM CTL Coenzyme        Q10    -   24. Hypoxia+12.5 mM Lactic Acid+22 mM Glucose+100 μM CTL        Coenzyme Q10

In certain situations, cross talk or ECS experiments between differentcancer cells (e.g., HepG2 and PaCa2) may be conducted for severalinter-related purposes. In some embodiments that involve cross talk,experiments conducted on the cell models are designed to determinemodulation of cellular state or function of one cell system orpopulation (e.g., Hepatocarcinoma cell HepG2) by another cell system orpopulation (e.g., Pancreatic cancer PaCa2) under defined treatmentconditions (e.g., hyperglycemia, hypoxia (ischemia)). According to atypical setting, a first cell system/population is contacted by anexternal stimulus components, such as a candidate molecule (e.g., asmall drug molecule, a protein) or a candidate condition (e.g., hypoxia,high glucose environment). In response, the first cell system/populationchanges its transcriptome, proteome, metabolome, and/or interactome,leading to changes that can be readily detected both inside and outsidethe cell. For example, changes in transcriptome can be measured by thetranscription level of a plurality of target mRNAs; changes in proteomecan be measured by the expression level of a plurality of targetproteins; and changes in metabolome can be measured by the level of aplurality of target metabolites by assays designed specifically forgiven metabolites. Alternatively, the above referenced changes inmetabolome and/or proteome, at least with respect to certain secretedmetabolites or proteins, can also be measured by their effects on thesecond cell system/population, including the modulation of thetranscriptome, proteome, metabolome, and interactome of the second cellsystem/population. Therefore, the experiments can be used to identifythe effects of the molecule(s) of interest secreted by the first cellsystem/population on a second cell system/population under differenttreatment conditions. The experiments can also be used to identify anyproteins that are modulated as a result of signaling from the first cellsystem (in response to the external stimulus component treatment) toanother cell system, by, for example, differential screening ofproteomics. The same experimental setting can also be adapted for areverse setting, such that reciprocal effects between the two cellsystems can also be assessed. In general, for this type of experiment,the choice of cell line pairs is largely based on the factors such asorigin, disease state and cellular function.

Although two-cell systems are typically involved in this type ofexperimental setting, similar experiments can also be designed for morethan two cell systems by, for example, immobilizing each distinct cellsystem on a separate solid support.

The custom built cancer model may be established and used throughout thesteps of the Platform Technology of the invention to ultimately identifya causal relationship unique in the biological system, by carrying outthe steps described herein. It will be understood by the skilledartisan, however, that a custom built cancer model that is used togenerate an initial, “first generation” consensus causal relationshipnetwork can continually evolve or expand over time, e.g., by theintroduction of additional cancer or normal cell lines and/or additionalcancer conditions. Additional data from the evolved cancer model, i.e.,data from the newly added portion(s) of the cancer model, can becollected. The new data collected from an expanded or evolved cancermodel, i.e., from newly added portion(s) of the cancer model, can thenbe introduced to the data sets previously used to generate the “firstgeneration” consensus causal relationship network in order to generate amore robust “second generation” consensus causal relationship network.New causal relationships unique to the cancer state (or unique to theresponse of the cancer state to a perturbation) can then be identifiedfrom the “second generation” consensus causal relationship network. Inthis way, the evolution of the cancer model provides an evolution of theconsensus causal relationship networks, thereby providing new and/ormore reliable insights into the determinative drivers (or modulators) ofthe cancer state.

(iii) Diabetes/Obesity/Cardiovascular Disease Cell Models

Other examples of a biological system or process are diabetes, obesityand cardiovascular disease. As with cancer, the related disease statesof diabetes, obesity and cardiovascular disease are complicatedpathological conditions characterized by multiple unique aspects. Itwould be of interest to identify the proteins/pathways driving thepathogenesis of diabetes/obesity/cardiovascular disease. It would alsobe of interest to decipher the unique response of cells associated withdiabetes/obesity/cardiovascular disease to drug treatment as compared tothe responses of normal cells. To this end, a customdiabetes/obesity/cardiovascular model may be established to simulate anenvironment experienced by disease-relevant cells, by choosingappropriate cell lines and creating cell culture conditions that mimic acharacteristic aspect of the disease state or process. For example, cellculture conditions may be selected that closely approximatehyperglycemia, hyperlipidemia, hyperinsulinemia, hypoxia or lactic-acidrich conditions.

Any cells relevant to diabetes/obesity/cardiovascular disease may beincluded in the diabetes/obesity/cardiovascular disease model. Examplesof cells relevant to diabetes/obesity/cardiovascular disease include,for example, adipocytes, myotubes, hepatocytes, aortic smooth musclecells (HASMC) and proximal tubular cells (e.g., HK2). Multiple celltypes of the same or different origin, as opposed to a single cell type,may be included in the diabetes/obesity/cardiovascular disease model. Inone embodiment, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more different cell typesare included in the diabetes/obesity/cardiovascular disease model. Allvalues presented in the foregoing list can also be the upper or lowerlimit of ranges, that are intended to be a part of this invention, e.g.,between 1 and 5, 1 and 10, 2 and 5, or 5 and 15 different cell celltypes. In one embodiment, the cells are all of the same type, e.g., alladipocytes, but are different established cell lines, e.g., differentestablished adipocyte cell lines. Numerous other cell types that areinvolved in the diabetes/obesity/cardiovascular disease state are knownin the art, and any such cells may be included in adiabetes/obesity/cardiovascular disease model of the invention.

Diabetes/obesity/cardiovascular disease cell models of the invention mayinclude one or more “control cells.” In one embodiment, a control cellmay be an untreated or unperturbed disease-relevant cell, e.g., a cellthat is not exposed to a hyperlipidemic or hyperinsulinemic condition.In another embodiment, a “control cell” may be a non-disease relevantcell, such as an epithelial cell. Any one of numerous non-diseaserelevant cells may be included in the cell model. In one embodiment, atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 45, 50 or more different non-disease relevant celltypes are included in the cell model. All values presented in theforegoing list can also be the upper or lower limit of ranges, that areintended to be a part of this invention, e.g., between 1 and 5, 1 and10, 2 and 5, or 5 and 15 different non-disease relevant cell lines orcell types. In one embodiment, the non-disease relevant cells are all ofthe same type, e.g., all healthy epithelial or breast cells, but aredifferent established cell lines, e.g., different established cell linesof epithelial or breast cells. In one embodiment, as a control, one ormore non-disease relevant cell lines are cultured under similarconditions, and/or are exposed to the same perturbation, as the diseaserelevant cells of the cell model in order to identify proteins orpathways unique to diabetes/obesity/cardiovascular disease.

A custom diabetes/obesity/cardiovascular disease model may also comprisecell culture conditions that mimic a characteristic aspect of (representthe pathophysiology of) the diabetes/obesity/cardiovascular diseasestate or process. For example, cell culture conditions may be selectedthat closely approximate the conditions of a cell relevant todiabetes/obesity/cardiovascular disease in its environment in vivo, orto mimic various aspects of diabetes/obesity/cardiovascular disease. Insome instances the cell culture conditions are stress conditions.

Exemplary conditions that represent the pathophysiology ofdiabetes/obesity/cardiovascular disease include, for example, any one ormore of hypoxia, lactic acid rich conditions, hyperglycemia,hyperlimidemia and hyperinsulinemia. Hypoxia can be induced in cells incells using art-recognized methods. For example, hypoxia can be inducedby placing cell systems in a Modular Incubator Chamber (MIC-101,Billups-Rothenberg Inc. Del Mar, Calif.), which can be flooded with anindustrial gas mix containing 5% CO₂, 2% O₂ and 93% nitrogen. Effectscan be measured after a pre-determined period, e.g., at 24 hours afterhypoxia treatment, with and without additional external stimuluscomponents (e.g., CoQ10 at 0, 50, or 100 μM).

Likewise, lactic acid treatment of cells mimics a cellular environmentwhere glycolysis activity is high. Lactic acid induced stress can beinvestigated at a final lactic acid concentration of about 12.5 mM at apre-determined time, e.g., at 24 hours, with or without additionalexternal stimulus components (e.g., CoQ10 at 0, 50, or 100 μM).Hyperglycemia is a condition found in diabetes. Exposing subject cellsto a typical hyperglycemic condition may include adding 10% culturegrade glucose to suitable media, such that the final concentration ofglucose in the media is about 22 mM. Hyperlipidemia is a condition foundin obesity and cardiovascular disease. The hyperlipidemic conditions canbe provided by culturing cells in media containing 0.15 mM sodiumpalmitate. Hyperinsulinemia is a condition found in diabetes. Thehyperinsulinemic conditions may be induced by culturing the cells inmedia containing 1000 nM insulin.

Additional conditions that represent the pathophysiology ofdiabetes/obesity/cardiovascular disease include, for example, any one ormore of inflammation, endoplasmic reticulum stress, mitochondrial stressand peroxisomal stress. Methods for creating an inflammatory-likecondition in cells are known in the art. For example, an inflammatorycondition may be simulated by culturing cells in the presence ofTNFalpha and or IL-6. Methods for creating conditions simulatingendoplasmic reticulum stress are also known in the art. For example, aconditions simulating endoplasmic reticulum stress may be created byculturing cells in the presence of thapsigargin and/or tunicamycin.Methods for creating conditions simulating mitochondrial stress are alsoknown in the art. For example, a conditions simulating mitochondrialstress may be created by culturing cells in the presence of rapamycinand/or galactose. Methods for creating conditions simulating peroxisomalstress are also known in the art. For example, a conditions simulatingperoxisomal stress may be created by culturing cells in the presence ofabscisic acid.

Individual conditions reflecting different aspects ofdiabetes/obesity/cardiovascular disease may be investigated separatelyin the custom built diabetes/obesity/cardiovascular disease model,and/or may be combined together. In one embodiment, combinations of atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or moreconditions reflecting or simulating different aspects ofdiabetes/obesity/cardiovascular disease are investigated in the custombuilt diabetes/obesity/cardiovascular disease model. In one embodiment,individual conditions and, in addition, combinations of at least 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50 or more of the conditionsreflecting or simulating different aspects ofdiabetes/obesity/cardiovascular disease are investigated in the custombuilt diabetes/obesity/cardiovascular disease model. All valuespresented in the foregoing list can also be the upper or lower limit ofranges, that are intended to be a part of this invention, e.g., between1 and 5, 1 and 10, 1 and 20, 1 and 30, 2 and 5, 2 and 10, 5 and 10, 1and 20, 5 and 20, 10 and 20, 10 and 25, 10 and 30 or 10 and 50 differentconditions.

Once the custom cell model is built, one or more “perturbations” may beapplied to the system, such as genetic variation from patient topatient, or with/without treatment by certain drugs or pro-drugs. SeeFIG. 15D. The effects of such perturbations to the system, including theeffect on diabetes/obesity/cardiovascular disease related cells, can bemeasured using various art-recognized or proprietary means, as describedin section III.B below.

In an exemplary experiment, each of adipocytes, myotubes, hepatocytes,aortic smooth muscle cells (HASMC) and proximal tubular cells (HK2), areconditioned in each of hyperglycemia, hypoxia, hyperlipidemia,hyperinsulinemia, and lactic acid-rich conditions, as well as in allcombinations of two, three, four and all five conditions, and inaddition with or without an environmental perturbation, specificallytreatment by Coenzyme Q10. In addition to exemplary combinations ofconditions described above in the context of the cancer model, listedherein below are some additional exemplary combinations of conditions,with or without a perturbation, e.g., Coenzyme Q10 treatment, which canbe used to treat the diabetes/obesity/cardiovascular disease relevantcells (and/or control cells) of the diabetes/obesity/cardiovasculardisease cell model. These are merely intended to be exemplary, and theskilled artisan will appreciate that any individual and/or combinationof the above-mentioned conditions that represent the pathophysiology ofdiabetes/obesity/cardiovascular disease may be employed in the cellmodel to produce output data sets. Other combinations can be readilyformulated depending on the specific interrogative biological assessmentthat is being conducted.

-   -   1. Media only    -   2. 50 μM CTL Coenzyme Q10    -   3. 100 μM CTL Coenzyme Q10    -   4. 0.15 mM sodium palmitate    -   5. 0.15 mM sodium palmitate+50 μM CTL Coenzyme Q10    -   6. 0.15 mM sodium palmitate+100 μM CTL Coenzyme Q10    -   7. 1000 nM insulin    -   8. 1000 nM insulin+50 μM CTL Coenzyme Q10    -   9. 1000 nM insulin+100 μM CTL Coenzyme Q10    -   10. 1000 nM insulin+0.15 mM sodium palmitate    -   11. 1000 nM insulin+0.15 mM sodium palmitate+50 μM CTL Coenzyme        Q10    -   12. 1000 nM insulin+0.15 mM sodium palmitate+100 μM CTL Coenzyme        Q10

In certain situations, cross talk or ECS experiments between differentdisease-relevant cells (e.g., HASMC and HK2 cells, or liver cells andadipocytes) may be conducted for several inter-related purposes. In someembodiments that involve cross talk, experiments conducted on the cellmodels are designed to determine modulation of cellular state orfunction of one cell system or population (e.g., liver cells) by anothercell system or population (e.g., adipocytes) under defined treatmentconditions (e.g., hyperglycemia, hypoxia, hyperlipidemia,hyperinsulinemia). According to a typical setting, a first cellsystem/population is contacted by an external stimulus components, suchas a candidate molecule (e.g., a small drug molecule, a protein) or acandidate condition (e.g., hypoxia, high glucose environment). Inresponse, the first cell system/population changes its transcriptome,proteome, metabolome, and/or interactome, leading to changes that can bereadily detected both inside and outside the cell. For example, changesin transcriptome can be measured by the transcription level of aplurality of target mRNAs; changes in proteome can be measured by theexpression level of a plurality of target proteins; and changes inmetabolome can be measured by the level of a plurality of targetmetabolites by assays designed specifically for given metabolites.Alternatively, the above referenced changes in metabolome and/orproteome, at least with respect to certain secreted metabolites orproteins, can also be measured by their effects on the second cellsystem/population, including the modulation of the transcriptome,proteome, metabolome, and interactome of the second cellsystem/population. Therefore, the experiments can be used to identifythe effects of the molecule(s) of interest secreted by the first cellsystem/population on a second cell system/population under differenttreatment conditions. The experiments can also be used to identify anyproteins that are modulated as a result of signaling from the first cellsystem (in response to the external stimulus component treatment) toanother cell system, by, for example, differential screening ofproteomics. The same experimental setting can also be adapted for areverse setting, such that reciprocal effects between the two cellsystems can also be assessed. In general, for this type of experiment,the choice of cell line pairs is largely based on the factors such asorigin, disease state and cellular function.

Although two-cell systems are typically involved in this type ofexperimental setting, similar experiments can also be designed for morethan two cell systems by, for example, immobilizing each distinct cellsystem on a separate solid support.

The custom built diabetes/obesity/cardiovascular disease model may beestablished and used throughout the steps of the Platform Technology ofthe invention to ultimately identify a causal relationship unique to thediabetes/obesity/cardiovascular disease state, by carrying out the stepsdescribed herein. It will be understood by the skilled artisan, however,that just as with a cancer model, a custom builtdiabetes/obesity/cardiovascular disease model that is used to generatean initial, “first generation” consensus causal relationship network cancontinually evolve or expand over time, e.g., by the introduction ofadditional disease-relevant cell lines and/or additionaldisease-relevant conditions. Additional data from the evolveddiabetes/obesity/cardiovascular disease model, i.e., data from the newlyadded portion(s) of the cancer model, can be collected. The new datacollected from an expanded or evolved model, i.e., from newly addedportion(s) of the model, can then be introduced to the data setspreviously used to generate the “first generation” consensus causalrelationship network in order to generate a more robust “secondgeneration” consensus causal relationship network. New causalrelationships unique to the diabetes/obesity/cardiovascular diseasestate (or unique to the response of the diabetes/obesity/cardiovasculardisease state to a perturbation) can then be identified from the “secondgeneration” consensus causal relationship network. In this way, theevolution of the diabetes/obesity/cardiovascular disease model providesan evolution of the consensus causal relationship networks, therebyproviding new and/or more reliable insights into the determinativedrivers (or modulators) of the diabetes/obesity/cardiovascular diseasestate.

B. Use of Cell Models for Interrogative Biological Assessments

The methods and cell models provided in the present invention may beused for, or applied to, any number of “interrogative biologicalassessments.” Use of the methods of the invention for an interrogativebiological assessment facilitates the identification of “modulators” ordeterminative cellular process “drivers” of a biological system.

As used herein, an “interrogative biological assessment” may include theidentification of one or more modulators of a biological system, e.g.,determinative cellular process “drivers,” (e.g., an increase or decreasein activity of a biological pathway, or key members of the pathway, orkey regulators to members of the pathway) associated with theenvironmental perturbation or external stimulus component, or a uniquecausal relationship unique in a biological system or process. It mayfurther include additional steps designed to test or verify whether theidentified determinative cellular process drivers are necessary and/orsufficient for the downstream events associated with the environmentalperturbation or external stimulus component, including in vivo animalmodels and/or in vitro tissue culture experiments.

In certain embodiments, the interrogative biological assessment is thediagnosis or staging of a disease state, wherein the identifiedmodulators of a biological system, e.g., determinative cellular processdrivers (e.g., cross-talk differentials or causal relationships uniquein a biological system or process) represent either disease markers ortherapeutic targets that can be subject to therapeutic intervention. Thesubject interrogative biological assessment is suitable for any diseasecondition in theory, but may found particularly useful in areas such asoncology/cancer biology, diabetes, obesity, cardiovascular disease, andneurological conditions (especially neuro-degenerative diseases, suchas, without limitation, Alzheimer's disease, Parkinson's disease,Huntington's disease, Amyotrophic lateral sclerosis (ALS), and agingrelated neurodegeneration).

In certain embodiments, the interrogative biological assessment is thedetermination of the efficacy of a drug, wherein the identifiedmodulators of a biological system, e.g., determinative cellular processdriver (e.g., cross-talk differentials or causal relationships unique ina biological system or process) may be the hallmarks of a successfuldrug, and may in turn be used to identify additional agents, such asMIMs or epishifters, for treating the same disease condition.

In certain embodiments, the interrogative biological assessment is theidentification of drug targets for preventing or treating infection(e.g., bacterial or viral infection), wherein the identifieddeterminative cellular process driver (e.g., cellular cross-talkdifferentials or causal relationships unique in a biological system orprocess) may be markers/indicators or key biological molecules causativeof the infective state, and may in turn be used to identifyanti-infective agents.

In certain embodiments, the interrogative biological assessment is theassessment of a molecular effect of an agent, e.g., a drug, on a givendisease profile, wherein the identified modulators of a biologicalsystem, e.g., determinative cellular process driver (e.g., cellularcross-talk differentials or causal relationships unique in a biologicalsystem or process) may be an increase or decrease in activity of one ormore biological pathways, or key members of the pathway(s), or keyregulators to members of the pathway(s), and may in turn be used, e.g.,to predict the therapeutic efficacy of the agent for the given disease.

In certain embodiments, the interrogative biological assessment is theassessment of the toxicological profile of an agent, e.g., a drug, on acell, tissue, organ or organism, wherein the identified modulators of abiological system, e.g., determinative cellular process driver (e.g.,cellular cross-talk differentials or causal relationships unique in abiological system or process) may be indicators of toxicity, e.g.,cytotoxicity, and may in turn be used to predict or identify thetoxicological profile of the agent. In one embodiment, the identifiedmodulators of a biological system, e.g., determinative cellular processdriver (e.g., cellular cross-talk differentials or causal relationshipsunique in a biological system or process) is an indicator ofcardiotoxicity of a drug or drug candidate, and may in turn be used topredict or identify the cardiotoxicological profile of the drug or drugcandidate.

In certain embodiments, the interrogative biological assessment is theidentification of drug targets for preventing or treating a disease ordisorder caused by biological weapons, such as disease-causing protozoa,fungi, bacteria, protests, viruses, or toxins, wherein the identifiedmodulators of a biological system, e.g., determinative cellular processdriver (e.g., cellular cross-talk differentials or causal relationshipsunique in a biological system or process) may be markers/indicators orkey biological molecules causative of said disease or disorder, and mayin turn be used to identify biodefense agents.

In certain embodiments, the interrogative biological assessment is theidentification of targets for anti-aging agents, such as anti-agingcosmetics, wherein the identified modulators of a biological system,e.g., determinative cellular process driver (e.g., cellular cross-talkdifferentials or causal relationships unique in a biological system orprocess) may be markers or indicators of the aging process, particularlythe aging process in skin, and may in turn be used to identifyanti-aging agents.

In one exemplary cell model for aging that is used in the methods of theinvention to identify targets for anti-aging cosmetics, the cell modelcomprises an aging epithelial cell that is, for example, treated with UVlight (an environmental perturbation or external stimulus component),and/or neonatal cells, which are also optionally treated with UV light.In one embodiment, a cell model for aging comprises a cellularcross-talk system. In one exemplary two-cell cross-talk systemestablished to identify targets for anti-aging cosmetics, an agingepithelial cell (first cell system) may be treated with UV light (anexternal stimulus component), and changes, e.g., proteomic changesand/or functional changes, in a neonatal cell (second cell system)resulting from contacting the neonatal cells with conditioned medium ofthe treated aging epithelial cell may be measured, e.g., proteomechanges may be measured using conventional quantitative massspectrometry, or a causal relationship unique in aging may be identifiedfrom a causal relationship network generated from the data.

V. Proteomic Sample Analysis

In certain embodiments, the subject method employs large-scalehigh-throughput quantitative proteomic analysis of hundreds of samplesof similar character, and provides the data necessary for identifyingthe cellular output differentials.

There are numerous art-recognized technologies suitable for thispurpose. An exemplary technique, iTRAQ analysis in combination with massspectrometry, is briefly described below.

To provide reference samples for relative quantification with the iTRAQtechnique, multiple QC pools are created. Two separate QC pools,consisting of aliquots of each sample, were generated from the Cell #1and Cell #2 samples—these samples are denoted as QCS1 and QCS2, and QCP1and QCP2 for supernatants and pellets, respectively. In order to allowfor protein concentration comparison across the two cell lines, cellpellet aliquots from the QC pools described above are combined in equalvolumes to generate reference samples (QCP).

The quantitative proteomics approach is based on stable isotope labelingwith the 8-plex iTRAQ reagent and 2D-LC MALDI MS/MS for peptideidentification and quantification. Quantification with this technique isrelative: peptides and proteins are assigned abundance ratios relativeto a reference sample. Common reference samples in multiple iTRAQexperiments facilitate the comparison of samples across multiple iTRAQexperiments.

To implement this analysis scheme, six primary samples and two controlpool samples are combined into one 8-plex iTRAQ mix, with the controlpool samples labeled with 113 and 117 reagents according to themanufacturer's suggestions. This mixture of eight samples is thenfractionated by two-dimensional liquid chromatography; strong cationexchange (SCX) in the first dimension, and reversed-phase HPLC in thesecond dimension. The HPLC eluent is directly fractionated onto MALDIplates, and the plates are analyzed on an MDS SCIEX/AB 4800 MALDITOF/TOF mass spectrometer.

In the absence of additional information, it is assumed that the mostimportant changes in protein expression are those within the same celltypes under different treatment conditions. For this reason, primarysamples from Cell#1 and Cell#2 are analyzed in separate iTRAQ mixes. Tofacilitate comparison of protein expression in Cell#1 vs. Cell#2samples, universal QCP samples are analyzed in the available “iTRAQslots” not occupied by primary or cell line specific QC samples (QC1 andQC2).

A brief overview of the laboratory procedures employed is providedherein.

A. Protein Extraction from Cell Supernatant Samples

For cell supernatant samples (CSN), proteins from the culture medium arepresent in a large excess over proteins secreted by the cultured cells.In an attempt to reduce this background, upfront abundant proteindepletion was implemented. As specific affinity columns are notavailable for bovine or horse serum proteins, an anti-human IgY14 columnwas used. While the antibodies are directed against human proteins, thebroad specificity provided by the polyclonal nature of the antibodieswas anticipated to accomplish depletion of both bovine and equineproteins present in the cell culture media that was used.

A 200-μl aliquot of the CSN QC material is loaded on a 10-mL IgY14depletion column before the start of the study to determine the totalprotein concentration (Bicinchoninic acid (BCA) assay) in theflow-through material. The loading volume is then selected to achieve adepleted fraction containing approximately 40 μg total protein.

B. Protein Extraction from Cell Pellets

An aliquot of Cell #1 and Cell #2 is lysed in the “standard” lysisbuffer used for the analysis of tissue samples at BGM, and total proteincontent is determined by the BCA assay. Having established the proteincontent of these representative cell lystates, all cell pellet samples(including QC samples described in Section 1.1) were processed to celllysates. Lysate amounts of approximately 40 μg of total protein werecarried forward in the processing workflow.

C. Sample Preparation for Mass Spectrometry

Sample preparation follows standard operating procedures and constituteof the following:

-   -   Reduction and alkylation of proteins    -   Protein clean-up on reversed-phase column (cell pellets only)    -   Digestion with trypsin    -   iTRAQ labeling    -   Strong cation exchange chromatography—collection of six        fractions (Agilent 1200 system)    -   HPLC fractionation and spotting to MALDI plates (Dionex        Ultimate3000/Probot system)

D. MALDI MS and MS/MS

HPLC-MS generally employs online ESI MS/MS strategies. BG Medicine usesan off-line LC-MALDI MS/MS platform that results in better concordanceof observed protein sets across the primary samples without the need ofinjecting the same sample multiple times. Following first pass datacollection across all iTRAQ mixes, since the peptide fractions areretained on the MALDI target plates, the samples can be analyzed asecond time using a targeted MS/MS acquisition pattern derived fromknowledge gained during the first acquisition. In this manner, maximumobservation frequency for all of the identified proteins is accomplished(ideally, every protein should be measured in every iTRAQ mix).

E. Data Processing

The data processing process within the BGM Proteomics workflow can beseparated into those procedures such as preliminary peptideidentification and quantification that are completed for each iTRAQ mixindividually (Section 1.5.1) and those processes (Section 1.5.2) such asfinal assignment of peptides to proteins and final quantification ofproteins, which are not completed until data acquisition is completedfor the project.

The main data processing steps within the BGM Proteomics workflow are:

-   -   Peptide identification using the Mascot (Matrix Sciences)        database search engine    -   Automated in house validation of Mascot IDs    -   Quantification of peptides and preliminary quantification of        proteins    -   Expert curation of final dataset    -   Final assignment of peptides from each mix into a common set of        proteins using the automated PVT tool    -   Outlier elimination and final quantification of proteins

(i) Data Processing of Individual iTRAQ Mixes

As each iTRAQ mix is processed through the workflow the MS/MS spectraare analyzed using proprietary BGM software tools for peptide andprotein identifications, as well as initial assessment of quantificationinformation. Based on the results of this preliminary analysis, thequality of the workflow for each primary sample in the mix is judgedagainst a set of BGM performance metrics. If a given sample (or mix)does not pass the specified minimal performance metrics, and additionalmaterial is available, that sample is repeated in its entirety and it isdata from this second implementation of the workflow that isincorporated in the final dataset.

(ii) Peptide Identification

MS/MS spectra was searched against the Uniprot protein sequence databasecontaining human, bovine, and horse sequences augmented by commoncontaminant sequences such as porcine trypsin. The details of the Mascotsearch parameters, including the complete list of modifications, aregiven in Table 3.

TABLE 3 Mascot Search Parameters Precursor mass tolerance 100 ppmFragment mass tolerance 0.4 Da Variable modifications N-term iTRAQ8Lysine iTRAQ8 Cys carbamidomethyl Pyro-Glu (N-term) Pyro-CarbamidomethylCys (N-term) Deamidation (N only) Oxidation (M) Enzyme specificity FullyTryptic Number of missed tryptic sites 2 allowed Peptide rank considered1

After the Mascot search is complete, an auto-validation procedure isused to promote (i.e., validate) specific Mascot peptide matches.Differentiation between valid and invalid matches is based on theattained Mascot score relative to the expected Mascot score and thedifference between the Rank 1 peptides and Rank 2 peptide Mascot scores.The criteria required for validation are somewhat relaxed if the peptideis one of several matched to a single protein in the iTRAQ mix or if thepeptide is present in a catalogue of previously validated peptides.

(iii) Peptide and Protein Quantification

The set of validated peptides for each mix is utilized to calculatepreliminary protein quantification metrics for each mix. Peptide ratiosare calculated by dividing the peak area from the iTRAQ label (i.e., m/z114, 115, 116, 118, 119, or 121) for each validated peptide by the bestrepresentation of the peak area of the reference pool (QC1 or QC2). Thispeak area is the average of the 113 and 117 peaks provided both samplespass QC acceptance criteria. Preliminary protein ratios are determinedby calculating the median ratio of all “useful” validated peptidesmatching to that protein. “Useful” peptides are fully iTRAQ labeled (allN-terminal are labeled with either Lysine or PyroGlu) and fully Cysteinelabeled (i.e., all Cys residues are alkylated with Carbamidomethyl orN-terminal Pyro-cmc).

(iv) Post-Acquisition Processing

Once all passes of MS/MS data acquisition are complete for every mix inthe project, the data is collated using the three steps discussed belowwhich are aimed at enabling the results from each primary sample to besimply and meaningfully compared to that of another.

(v) Global Assignment of Peptide Sequences to Proteins

Final assignment of peptide sequences to protein accession numbers iscarried out through the proprietary Protein Validation Tool (PVT). ThePVT procedure determines the best, minimum non-redundant protein set todescribe the entire collection of peptides identified in the project.This is an automated procedure that has been optimized to handle datafrom a homogeneous taxonomy.

Protein assignments for the supernatant experiments were manuallycurated in order to deal with the complexities of mixed taxonomies inthe database. Since the automated paradigm is not valid for cellcultures grown in bovine and horse serum supplemented media, extensivemanual curation is necessary to minimize the ambiguity of the source ofany given protein.

(vi) Normalization of Peptide Ratios

The peptide ratios for each sample are normalized based on the method ofVandesompele et al. Genome Biology, 2002, 3(7), research 0034.1-11. Thisprocedure is applied to the cell pellet measurements only. For thesupernatant samples, quantitative data are not normalized consideringthe largest contribution to peptide identifications coming from themedia.

(vii) Final Calculation of Protein Ratios

A standard statistical outlier elimination procedure is used to removeoutliers from around each protein median ratio, beyond the 1.96 σ levelin the log-transformed data set. Following this elimination process, thefinal set of protein ratios are (re-)calculated.

VI. Markers of the Invention and Uses Thereof

The present invention is based, at least in part, on the identificationof novel biomarkers that are associated with a biological system, suchas a disease process, or response of a biological system to aperturbation, such as a therapeutic agent.

In particular, the invention relates to markers (hereinafter “markers”or “markers of the invention”), which are described in the examples. Theinvention provides nucleic acids and proteins that are encoded by orcorrespond to the markers (hereinafter “marker nucleic acids” and“marker proteins,” respectively). These markers are particularly usefulin diagnosing disease states; prognosing disease states; developing drugtargets for varies disease states; screening for the presence oftoxicity, preferably drug-induced toxicity, e.g., cardiotoxicity;identifying an agent that cause or is at risk for causing toxicity;identifying an agent that can reduce or prevent drug-induced toxicity;alleviating, reducing or preventing drug-induced cardiotoxicity; andidentifying markers predictive of drug-induced cardiotoxicity.

A “marker” is a gene whose altered level of expression in a tissue orcell from its expression level in normal or healthy tissue or cell isassociated with a disease state such as cancer, diabetes, obesity,cardiovescular disease, or a toxicity state, such as a drug-inducedtoxicity, e.g., cardiotoxicity. A “marker nucleic acid” is a nucleicacid (e.g., mRNA, cDNA) encoded by or corresponding to a marker of theinvention. Such marker nucleic acids include DNA (e.g., cDNA) comprisingthe entire or a partial sequence of any of the genes that are markers ofthe invention or the complement of such a sequence. Such sequences areknown to the one of skill in the art and can be found for example, onthe NIH government pubmed website. The marker nucleic acids also includeRNA comprising the entire or a partial sequence of any of the genemarkers of the invention or the complement of such a sequence, whereinall thymidine residues are replaced with uridine residues. A “markerprotein” is a protein encoded by or corresponding to a marker of theinvention. A marker protein comprises the entire or a partial sequenceof any of the marker proteins of the invention. Such sequences are knownto the one of skill in the art and can be found for example, on the NIHgovernment pubmed website. The terms “protein” and “polypeptide’ areused interchangeably.

A “disease state or toxic state associated” body fluid is a fluid which,when in the body of a patient, contacts or passes through sarcoma cellsor into which cells or proteins shed from sarcoma cells are capable ofpassing. Exemplary disease state or toxic state associated body fluidsinclude blood fluids (e.g. whole blood, blood serum, blood havingplatelets removed therefrom), and are described in more detail below.Disease state or toxic state associated body fluids are not limited to,whole blood, blood having platelets removed therefrom, lymph, prostaticfluid, urine and semen.

The “normal” level of expression of a marker is the level of expressionof the marker in cells of a human subject or patient not afflicted witha disease state or a toxicity state.

An “over-expression” or “higher level of expression” of a marker refersto an expression level in a test sample that is greater than thestandard error of the assay employed to assess expression, and ispreferably at least twice, and more preferably three, four, five, six,seven, eight, nine or ten times the expression level of the marker in acontrol sample (e.g., sample from a healthy subject not having themarker associated a disease state or a toxicity state, e.g., cancer,diabetes, obesity, cardiovescular disease, and cardiotoxicity) andpreferably, the average expression level of the marker in severalcontrol samples.

A “lower level of expression” of a marker refers to an expression levelin a test sample that is at least twice, and more preferably three,four, five, six, seven, eight, nine or ten times lower than theexpression level of the marker in a control sample (e.g., sample from ahealthy subjects not having the marker associated a disease state or atoxicity state, e.g., cancer, diabetes, obesity, cardiovescular disease,and cardiotoxicity) and preferably, the average expression level of themarker in several control samples.

A “transcribed polynucleotide” or “nucleotide transcript” is apolynucleotide (e.g. an mRNA, hnRNA, a cDNA, or an analog of such RNA orcDNA) which is complementary to or homologous with all or a portion of amature mRNA made by transcription of a marker of the invention andnormal post-transcriptional processing (e.g. splicing), if any, of theRNA transcript, and reverse transcription of the RNA transcript.

“Complementary” refers to the broad concept of sequence complementaritybetween regions of two nucleic acid strands or between two regions ofthe same nucleic acid strand. It is known that an adenine residue of afirst nucleic acid region is capable of forming specific hydrogen bonds(“base pairing”) with a residue of a second nucleic acid region which isantiparallel to the first region if the residue is thymine or uracil.Similarly, it is known that a cytosine residue of a first nucleic acidstrand is capable of base pairing with a residue of a second nucleicacid strand which is antiparallel to the first strand if the residue isguanine. A first region of a nucleic acid is complementary to a secondregion of the same or a different nucleic acid if, when the two regionsare arranged in an antiparallel fashion, at least one nucleotide residueof the first region is capable of base pairing with a residue of thesecond region. Preferably, the first region comprises a first portionand the second region comprises a second portion, whereby, when thefirst and second portions are arranged in an antiparallel fashion, atleast about 50%, and preferably at least about 75%, at least about 90%,or at least about 95% of the nucleotide residues of the first portionare capable of base pairing with nucleotide residues in the secondportion. More preferably, all nucleotide residues of the first portionare capable of base pairing with nucleotide residues in the secondportion.

“Homologous” as used herein, refers to nucleotide sequence similaritybetween two regions of the same nucleic acid strand or between regionsof two different nucleic acid strands. When a nucleotide residueposition in both regions is occupied by the same nucleotide residue,then the regions are homologous at that position. A first region ishomologous to a second region if at least one nucleotide residueposition of each region is occupied by the same residue. Homologybetween two regions is expressed in terms of the proportion ofnucleotide residue positions of the two regions that are occupied by thesame nucleotide residue. By way of example, a region having thenucleotide sequence 5′-ATTGCC-3′ and a region having the nucleotidesequence 5′-TATGGC-3′ share 50% homology. Preferably, the first regioncomprises a first portion and the second region comprises a secondportion, whereby, at least about 50%, and preferably at least about 75%,at least about 90%, or at least about 95% of the nucleotide residuepositions of each of the portions are occupied by the same nucleotideresidue. More preferably, all nucleotide residue positions of each ofthe portions are occupied by the same nucleotide residue.

“Proteins of the invention” encompass marker proteins and theirfragments; variant marker proteins and their fragments; peptides andpolypeptides comprising an at least 15 amino acid segment of a marker orvariant marker protein; and fusion proteins comprising a marker orvariant marker protein, or an at least 15 amino acid segment of a markeror variant marker protein.

The invention further provides antibodies, antibody derivatives andantibody fragments which specifically bind with the marker proteins andfragments of the marker proteins of the present invention. Unlessotherwise specified herewithin, the terms “antibody” and “antibodies”broadly encompass naturally-occurring forms of antibodies (e.g., IgG,IgA, IgM, IgE) and recombinant antibodies such as single-chainantibodies, chimeric and humanized antibodies and multi-specificantibodies, as well as fragments and derivatives of all of theforegoing, which fragments and derivatives have at least an antigenicbinding site. Antibody derivatives may comprise a protein or chemicalmoiety conjugated to an antibody.

In certain embodiments, the markers of the invention include one or moregenes (or proteins) selected from the group consisting of HSPA8, FLNB,PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1, DDX17,EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1,CANX, GRP78, GRP75, TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 andTAZ. In some embodiments, the markers are a combination of at least two,three, four, five, six, seven, eight, nine, ten, eleven, twelve,thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen,twenty, twenty-five, thirty, or more of the foregoing genes (orproteins). All values presented in the foregoing list can also be theupper or lower limit of ranges, that are intended to be a part of thisinvention, e.g., between 1 and 5, 1 and 10, 1 and 20, 1 and 30, 2 and 5,2 and 10, 5 and 10, 1 and 20, 5 and 20, 10 and 20, 10 and 25, and 30 ofthe foregoing genes (or proteins).

In one embodiment, the markers of the invention are genes or proteinsassociated with or involved in cancer. Such genes or proteins involvedin cancer include, for example, HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13,TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC,CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and/or CANX. In someembodiments, the markers of the invention are a combination of at leasttwo, three, four, five, six, seven, eight, nine, ten, eleven, twelve,thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen,twenty or more of the foregoing genes (or proteins). All valuespresented in the foregoing list can also be the upper or lower limit ofranges, that are intended to be a part of this invention, e.g., between1 and 5, 1 and 10, 1 and 20, 1 and 30, 2 and 5, 2 and 10, 5 and 10, 1and 20, 5 and 20, 10 and 20, 10 and 25, 10 and 30 of the foregoing genes(or proteins).

In one embodiment, the markers of the invention are genes or proteinsassociated with or involved in drug-induced toxicity. Such genes orproteins involved in drug-induced toxicity include, for example, GRP78,GRP75, TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and/or TAZ. Insome embodiments, the markers of the invention are a combination of atleast two, three, four, five, six, seven, eight, nine, ten of theforegoing genes (or proteins). All values presented in the foregoinglist can also be the upper or lower limit of ranges, that are intendedto be a part of this invention, e.g., between 1 and 5, 1 and 10, 1 and20, 1 and 30, 2 and 5, 2 and 10, 5 and 10, 1 and 20, 5 and 20, 10 and20, 10 and 25, 10 and 30 of the foregoing genes (or proteins).

A. Cardiotoxicity Associated Markers

The present invention is based, at least in part, on the identificationof novel biomarkers that are associated with drug-inducedcardiotoxicity. The invention is further based, at least in part, on thediscovery that Coenzyme Q10 is capable of reducing or preventingdrug-induced cardiotoxicity.

Accordingly, the invention provides methods for identifying an agentthat causes or is at risk for causing toxicity. In one embodiment, theagent is a drug or drug candidate. In one embodiment, the toxicity isdrug-induced toxicity, e.g., cardiotoxicity. In one embodiment, theagent is a drug or drug candidate for treating diabetes, obesity or acardiovascular disorder. In these methods, the amount of one or morebiomarkers/proteins in a pair of samples (a first sample not subject tothe drug treatment, and a second sample subjected to the drug treatment)is assessed. A modulation in the level of expression of the one or morebiomarkers in the second sample as compared to the first sample is anindication that the drug causes or is at risk for causing drug-inducedtoxicity, e.g., cardiotoxicity. In one embodiment, the one or morebiomarkers is selected from the group consisting of GRP78, GRP75, TIMP1,PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ. The methods of thepresent invention can be practiced in conjunction with any other methodused by the skilled practitioner to identify a drug at risk for causingdrug-induced cardiotoxocity.

Accordingly, in one aspect, the invention provides a method foridentifying a drug that causes or is at risk for causing drug-inducedtoxicity (e.g., cardiotoxicity), comprising: comparing (i) the level ofexpression of one or more biomarkers present in a first cell sampleobtained prior to the treatment with the drug; with (ii) the level ofexpression of the one or more biomarkers present in a second cell sampleobtained following the treatment with the drug; wherein the one or morebiomarkers is selected from the group consisting of GRP78, GRP75, TIMP1,PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ; wherein a modulation inthe level of expression of the one or more biomarkers in the secondsample as compared to the first sample is an indication that the drugcauses or is at risk for causing drug-induced toxicity (e.g.,cardiotoxicity).

In one embodiment, the drug-induced toxicity is drug-inducedcardiotoxicity. In one embodiment, the cells are cells of thecardiovascular system, e.g., cardiomyocytes. In one embodiment, thecells are diabetic cardiomyocytes. In one embodiment, the drug is a drugor candidate drug for treating diabetes, obesity or cardiovasculardisease.

In one embodiment, a modulation (e.g., an increase or a decrease) in thelevel of expression of one, two, three, four, five, six, seven, eight,nine or all ten of the biomarkers selected from the group consisting ofGRP78, GRP75, TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ inthe second sample as compared to the first sample is an indication thatthe drug causes or is at risk for causing drug-induced toxicity.

Methods for identifying an agent that can reduce or prevent drug-inducedtoxicity are also provided by the invention. In one embodiment, thedrug-induced toxicity is cardiotoxicity. In one embodiment, the drug isa drug or drug candidate for treating diabetes, obesity or acardiovascular disorder. In these methods, the amount of one or morebiomarkers in three samples (a first sample not subjected to the drugtreatment, a second sample subjected to the drug treatment, and a thirdsample subjected both to the drug treatment and the agent) is assessed.Approximately the same level of expression of the one or more biomarkersin the third sample as compared to the first sample is an indicationthat the agent can reduce or prevent drug-induced toxicity, e.g.,drug-induced cardiotoxicity. In one embodiment, the one or morebiomarkers is selected from the group consisting of GRP78, GRP75, TIMP1,PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ.

Using the methods described herein, a variety of molecules, particularlyincluding molecules sufficiently small to be able to cross the cellmembrane, may be screened in order to identify molecules which modulate,e.g., increase or decrease the expression and/or activity of a marker ofthe invention. Compounds so identified can be provided to a subject inorder to reduce, alleviate or prevent drug-induced toxicity in thesubject.

Accordingly, in another aspect, the invention provides a method foridentifying an agent that can reduce or prevent drug-induced toxicitycomprising: (i) determining the level of expression of one or morebiomarkers present in a first cell sample obtained prior to thetreatment with a toxicity inducing drug; (ii) determining the level ofexpression of the one or more biomarkers present in a second cell sampleobtained following the treatment with the toxicity inducing drug; (iii)determining the level of expression of the one or more biomarkerspresent in a third cell sample obtained following the treatment with thetoxicity inducing drug and the agent; and (iv) comparing the level ofexpression of the one or more biomarkers present in the third samplewith the first sample; wherein the one or more biomarkers is selectedfrom the group consisting of GRP78, GRP75, TIMP1, PTX3, HSP76, PDIA4,PDIA1, CA2D1, GPAT1 and TAZ; and wherein about the same level ofexpression of the one or more biomarkers in the third sample as comparedto the first sample is an indication that the agent can reduce orprevent drug-induced toxicity.

In one embodiment, the drug-induced toxicity is drug-inducedcardiotoxicity. In one embodiment, the cells are cells of thecardiovascular system, e.g., cardiomyocytes. In one embodiment, thecells are diabetic cardiomyocytes. In one embodiment, the drug is a drugor candidate drug for treating diabetes, obesity or cardiovasculardisease.

In one embodiment, about the same level of expression of one, two,three, four, five, six, seven, eight, nine or all ten of the biomarkersselected from the group consisting of GRP78, GRP75, TIMP1, PTX3, HSP76,PDIA4, PDIA1, CA2D1, GPAT1 and TAZ in the third sample as compared tothe first sample is an indication that the agent can reduce or preventdrug-induced toxicity.

The invention further provides methods for alleviating, reducing orpreventing drug-induced cardiotoxicity in a subject in need thereof,comprising administering to a subject (e.g., a mammal, a human, or anon-human animal) an agent identified by the screening methods providedherein, thereby reducing or preventing drug-induced cardiotoxicity inthe subject. In one embodiment, the agent is administered to a subjectthat has already been treated with a cardiotoxicity-inducing drug. Inone embodiment, the agent is administered to a subject at the same timeas treatment of the subject with a cardiotoxicity-inducing drug. In oneembodiment, the agent is administered to a subject prior to treatment ofthe subject with a cardiotoxicity-inducing drug.

The invention further provides methods for alleviating, reducing orpreventing drug-induced cardiotoxicity in a subject in need thereof,comprising administering Coenzyme Q10 to the subject (e.g., a mammal, ahuman, or a non-human animal), thereby reducing or preventingdrug-induced cardiotoxicity in the subject. In one embodiment, theCoenzyme Q10 is administered to a subject that has already been treatedwith a cardiotoxicity-inducing drug. In one embodiment, the Coenzyme Q10is administered to a subject at the same time as treatment of thesubject with a cardiotoxicity-inducing drug. In one embodiment, theCoenzyme Q10 is administered to a subject prior to treatment of thesubject with a cardiotoxicity-inducing drug. In one embodiment, thedrug-induced cardiotoxicity is associated with modulation of expressionof one, two, three, four, five, six, seven, eight, nine or all ten ofthe biomarkers selected from the group consisting of GRP78, GRP75,TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ. All valuespresented in the foregoing list can also be the upper or lower limit ofranges, that are intended to be a part of this invention, e.g., between1 and 5, 1 and 10, 2 and 5, 2 and 10, or 5 and 10 of the foregoing genes(or proteins).

The invention further provides biomarkers (e.g, genes and/or proteins)that are useful as predictive markers for cardiotoxicity, e.g.,drug-induced cardiotoxicity. These biomarkers include GRP78, GRP75,TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ. The ordinaryskilled artisan would, however, be able to identify additionalbiomarkers predictive of drug-induced cardiotoxicity by employing themethods described herein, e.g., by carrying out the methods described inExample 3 but by using a different drug known to induce cardiotoxicity.Exemplary drug-induced cardiotoxicity biomarkers of the invention arefurther described below.

GRP78 and GRP75 are also referred to as glucose response proteins. Theseproteins are associated with endo/sarcoplasmic reticulum stress (ERstress) of cardiomyocytes. SERCA, or sarcoendoplasmic reticulum calciumATPase, regulates Ca2+ homeostatsis in cardiac cells. Any disruption ofthese ATPase can lead to cardiac dysfunction and heart failure. Basedupon the data provided herein, GRP75 and GRP78 and the edges around themare novel predictors of drug induced cardiotoxicity.

TIMP1, also referred to as TIMP metalloprotease inhibitor 1, is involvedwith remodeling of extra cellular matrix in association with MMPs. TIMP1expression is correlated with fibrosis of the heart, and hypoxia ofvascular endothelial cells also induces TIMP1 expression. Based upon thedata provided herein, TIMP1 is a novel predictor of drug inducedcardiactoxicity

PTX3, also referred to as Pentraxin 3, belongs to the family of CReactive Proteins (CRP) and is a good marker of an inflammatorycondition of the heart. However, plasma PTX3 could also berepresentative of systemic inflammatory response due to sepsis or othermedical conditions. Based upon the data provided herein, PTX3 may be anovel marker of cardiac function or cardiotoxicity. Additionally, theedges associated with PTX 3 in the network could form a novel panel ofbiomarkers.

HSP76, also referred to as HSPA6, is only known to be expressed inendothelial cells and B lymphocytes. There is no known role for thisprotein in cardiac function. Based upon the data provided herein, HSP76may be a novel predictor of drug induced cardiotoxicity

PDIA4, PDIA1, also referred to as protein disulphide isomerase family Aproteins, are associated with ER stress response, like GRPs. There is noknown role for these proteins in cardiac function. Based upon the dataprovided herein, these proteins may be novel predictors of drug inducedcardiotoxicity.

CA2D1 is also referred to as calcium channel, voltage-dependent, alpha2/delta subunit. The alpha-2/delta subunit of voltage-dependent calciumchannel regulates calcium current density and activation/inactivationkinetics of the calcium channel. CA2D1 plays an important role inexcitation-contraction coupling in the heart. There is no known role forthis protein in cardiac function. Based upon the data provided herein,CA2D1 is a novel predictor of drug induced cardiotoxicity

GPAT1 is one of four known glycerol-3-phosphate acyltransferaseisoforms, and is located on the mitochondrial outer membrane, allowingreciprocal regulation with carnitine palmitoyltransferase-1. GPAT1 isupregulated transcriptionally by insulin and SREBP-1c and downregulatedacutely by AMP-activated protein kinase, consistent with a role intriacylglycerol synthesis. Based upon the data provided herein, GPAT1 isa novel predictor of drug induced cardiotoxicity.

TAZ, also referred to as Tafazzin, is highly expressed in cardiac andskeletal muscle. TAZ is involved in the metabolism of cardiolipin andfunctions as a phospholipid-lysophospholipid transacylase. Tafazzin isresponsible for remodeling of a phospholipid cardiolipin (CL), thesignature lipid of the mitochondrial inner membrane. Based upon the dataprovided herein, TAZ is a novel predictor of drug induced cardiotoxicity

B. Cancer Associated Markers

The present invention is based, at least in part, on the identificationof novel biomarkers that are associated with cancer. Such markersassociated in cancer include, for example, HSPA8, FLNB, PARK7,HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A,HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and/orCANX. In some embodiments, the markers of the invention are acombination of at least two, three, four, five, six, seven, eight, nine,ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen,eighteen, nineteen, twenty or more of the foregoing markers.

Accordingly, the invention provides methods for identifying an agentthat causes or is at risk for causing cancer. In one embodiment, theagent is a drug or drug candidate. In these methods, the amount of oneor more biomarkers/proteins in a pair of samples (a first sample notsubject to the drug treatment, and a second sample subjected to the drugtreatment) is assessed. A modulation in the level of expression of theone or more biomarkers in the second sample as compared to the firstsample is an indication that the drug causes or is at risk for causingcancer. In one embodiment, the one or more biomarkers is selected fromthe group consisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3,MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4,HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX. The methods of the presentinvention can be practiced in conjunction with any other method used bythe skilled practitioner to identify a drug at risk for causing thecancer.

In one aspect, the invention provides methods for assessing the efficacyof a therapy for treating a cancer in a subject, the method comprising:comparing the level of expression of one or more markers present in afirst sample obtained from the subject prior to administering at least aportion of the treatment regimen to the subject, wherein the one or moremarkers is selected from the group consisting of HSPA8, FLNB, PARK7,HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A,HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX;and the level of expression of the one or more markers present in asecond sample obtained from the subject following administration of atleast a portion of the treatment regimen, wherein a modulation in thelevel of expression of the one or more markers in the second sample ascompared to the first sample is an indication that the therapy isefficacious for treating the cancer in the subject.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides methods of assessing whether a subject isafflicted with a cancer, the method comprising: determining the level ofexpression of one or more markers present in a biological sampleobtained from the subject, wherein the one or more markers is selectedfrom the group consisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13,TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC,CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX; and comparing thelevel of expression of the one or more markers present in the biologicalsample obtained from the subject with the level of expression of the oneor more markers present in a control sample, wherein a modulation in thelevel of expression of the one or more markers in the biological sampleobtained from the subject relative to the level of expression of the oneor more markers in the control sample is an indication that the subjectis afflicted with cancer, thereby assessing whether the subject isafflicted with the cancer.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides methods of prognosing whether a subjectis predisposed to developing a cancer, the method comprising:determining the level of expression of one or more markers present in abiological sample obtained from the subject, wherein the one or moremarkers is selected from the group consisting of HSPA8, FLNB, PARK7,HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A,HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX;and comparing the level of expression of the one or more markers presentin the biological sample obtained from the subject with the level ofexpression of the one or more markers present in a control sample,wherein a modulation in the level of expression of the one or moremarkers in the biological sample obtained from the subject relative tothe level of expression of the one or more markers in the control sampleis an indication that the subject is predisposed to developing cancer,thereby prognosing whether the subject is predisposed to developing thecancer.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides methods of prognosing the recurrence of acancer in a subject, the method comprising: determining the level ofexpression of one or more markers present in a biological sampleobtained from the subject, wherein the one or more markers is selectedfrom the group consisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13,TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC,CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX; and comparing thelevel of expression of the one or more markers present in the biologicalsample obtained from the subject with the level of expression of the oneor more markers present in a control sample, wherein a modulation in thelevel of expression of the one or more markers in the biological sampleobtained from the subject relative to the level of expression of the oneor more markers in the control sample is an indication of the recurrenceof cancer, thereby prognosing the recurrence of the cancer in thesubject.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides methods of prognosing the survival of asubject with a cancer, the method comprising: determining the level ofexpression of one or more markers present in a biological sampleobtained from the subject, wherein the one or more markers is selectedfrom the group consisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13,TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC,CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX; and comparing thelevel of expression of the one or more markers present in the biologicalsample obtained from the subject with the level of expression of the oneor more markers present in a control sample, wherein a modulation in thelevel of expression of the one or more markers in the biological sampleobtained from the subject relative to the level of expression of the oneor more markers in the control sample is an indication of survival ofthe subject, thereby prognosing survival of the subject with the cancer.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides methods of monitoring the progression ofa cancer in a subject, the method comprising: comparing, the level ofexpression of one or more markers present in a first sample obtainedfrom the subject prior to administering at least a portion of atreatment regimen to the subject and the level of expression of the oneor more markers present in a second sample obtained from the subjectfollowing administration of at least a portion of the treatment regimen,wherein the one or more markers is selected from the group consisting ofHSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1,DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2,ATP5A1, and CANX, thereby monitoring the progression of the cancer inthe subject.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides methods of identifying a compound fortreating a cancer in a subject, the method comprising: obtaining abiological sample from the subject; contacting the biological samplewith a test compound; determining the level of expression of one or moremarkers present in the biological sample obtained from the subject,wherein the one or more markers is selected from the group consisting ofHSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1,DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2,ATP5A1, and CANX with a positive fold change and/or with a negative foldchange; comparing the level of expression of the one of more markers inthe biological sample with an appropriate control; and selecting a testcompound that decreases the level of expression of the one or moremarkers with a negative fold change present in the biological sampleand/or increases the level of expression of the one or more markers witha positive fold change present in the biological sample, therebyidentifying a compound for treating the cancer in a subject.

In one embodiment, the sample comprises a fluid obtained from thesubject. In one embodiment, the fluid is selected from the groupconsisting of blood fluids, vomit, saliva, lymph, cystic fluid, urine,fluids collected by bronchial lavage, fluids collected by peritonealrinsing, and gynecological fluids. In one embodiment, the sample is ablood sample or a component thereof.

In another embodiment, the sample comprises a tissue or componentthereof obtained from the subject. In one embodiment, the tissue isselected from the group consisting of bone, connective tissue,cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, andskin.

In one embodiment, the subject is a human.

In one embodiment, the level of expression of the one or more markers inthe biological sample is determined by assaying a transcribedpolynucleotide or a portion thereof in the sample. In one embodiment,wherein assaying the transcribed polynucleotide comprises amplifying thetranscribed polynucleotide.

In one embodiment, the level of expression of the marker in the subjectsample is determined by assaying a protein or a portion thereof in thesample. In one embodiment, the protein is assayed using a reagent whichspecifically binds with the protein.

In one embodiment, the level of expression of the one or more markers inthe sample is determined using a technique selected from the groupconsisting of polymerase chain reaction (PCR) amplification reaction,reverse-transcriptase PCR analysis, single-strand conformationpolymorphism analysis (SSCP), mismatch cleavage detection, heteroduplexanalysis, Southern blot analysis, Northern blot analysis, Western blotanalysis, in situ hybridization, array analysis, deoxyribonucleic acidsequencing, restriction fragment length polymorphism analysis, andcombinations or sub-combinations thereof, of said sample.

In one embodiment, the level of expression of the marker in the sampleis determined using a technique selected from the group consisting ofimmunohistochemistry, immunocytochemistry, flow cytometry, ELISA andmass spectrometry.

In one embodiment, the level of expression of a plurality of markers isdetermined.

In one embodiment, the subject is being treated with a therapy selectedfrom the group consisting of an environmental influencer compound,surgery, radiation, hormone therapy, antibody therapy, therapy withgrowth factors, cytokines, chemotherapy, allogenic stem cell therapy. Inone embodiment, the environmental influencer compound is a Coenzyme Q10molecule.

The invention further provides a kit for assessing the efficacy of atherapy for treating a cancer, the kit comprising reagents fordetermining the level of expression of at least one marker selected fromthe group consisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3,MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4,HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX and instructions for use ofthe kit to assess the efficacy of the therapy for treating the cancer.

The invention further provides a kit for assessing whether a subject isafflicted with a cancer, the kit comprising reagents for determining thelevel of expression of at least one marker selected from the groupconsisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS,NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1,HADHA, PHB2, ATP5A1, and CANX and instructions for use of the kit toassess whether the subject is afflicted with the cancer.

The invention further provides a kit for prognosing whether a subject ispredisposed to developing a cancer, the kit comprising reagents fordetermining the level of expression of at least one marker selected fromthe group consisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3,MIF, KARS, NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4,HSPA9, PARP1, HADHA, PHB2, ATP5A1, and CANX and instructions for use ofthe kit to prognose whether the subject is predisposed to developing thecancer.

The invention further provides a kit for prognosing the recurrence of acancer in a subject, the kit comprising reagents for assessing the levelof expression of at least one marker selected from the group consistingof HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS,LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA,PHB2, ATP5A1, and CANX and instructions for use of the kit to prognosethe recurrence of the cancer.

The invention further provides a kit for prognosing the recurrence of acancer, the kit comprising reagents for determining the level ofexpression of at least one marker selected from the group consisting ofHSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1,DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2,ATP5A1, and CANX and instructions for use of the kit to prognose therecurrence of the cancer.

The invention further provides a kit for prognosing the survival of asubject with a cancer, the kit comprising reagents for determining thelevel of expression of at least one marker selected from the groupconsisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS,NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1,HADHA, PHB2, ATP5A1, and CANX and instructions for use of the kit toprognose the survival of the subject with the cancer.

The invention further provides a kit for monitoring the progression of acancer in a subject, the kit comprising reagents for determining thelevel of expression of at least one marker selected from the groupconsisting of HSPA8, FLNB, PARK7, HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS,NARS, LGALS1, DDX17, EIF5A, HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1,HADHA, PHB2, ATP5A1, and CANX and instructions for use of the kit toprognose the progression of the cancer in a subject.

The kits of the invention may further comprising means for obtaining abiological sample from a subject, a control sample, and/or anenvironmental influencer compound

The means for determining the level of expression of at least one markermay comprises means for assaying a transcribed polynucleotide or aportion thereof in the sample and/or means for assaying a protein or aportion thereof in the sample.

In one embodiment, the kits comprises reagents for determining the levelof expression of a plurality of markers.

Various aspects of the invention are described in further detail in thefollowing subsections.

C. Isolated Nucleic Acid Molecules

One aspect of the invention pertains to isolated nucleic acid molecules,including nucleic acids which encode a marker protein or a portionthereof. Isolated nucleic acids of the invention also include nucleicacid molecules sufficient for use as hybridization probes to identifymarker nucleic acid molecules, and fragments of marker nucleic acidmolecules, e.g., those suitable for use as PCR primers for theamplification or mutation of marker nucleic acid molecules. As usedherein, the term “nucleic acid molecule” is intended to include DNAmolecules (e.g., cDNA or genomic DNA) and RNA molecules (e.g., mRNA) andanalogs of the DNA or RNA generated using nucleotide analogs. Thenucleic acid molecule can be single-stranded or double-stranded, butpreferably is double-stranded DNA.

An “isolated” nucleic acid molecule is one which is separated from othernucleic acid molecules which are present in the natural source of thenucleic acid molecule. In one embodiment, an “isolated” nucleic acidmolecule is free of sequences (preferably protein-encoding sequences)which naturally flank the nucleic acid (i.e., sequences located at the5′ and 3′ ends of the nucleic acid) in the genomic DNA of the organismfrom which the nucleic acid is derived. For example, in variousembodiments, the isolated nucleic acid molecule can contain less thanabout 5 kB, 4 kB, 3 kB, 2 kB, 1 kB, 0.5 kB or 0.1 kB of nucleotidesequences which naturally flank the nucleic acid molecule in genomic DNAof the cell from which the nucleic acid is derived. In anotherembodiment, an “isolated” nucleic acid molecule, such as a cDNAmolecule, can be substantially free of other cellular material, orculture medium when produced by recombinant techniques, or substantiallyfree of chemical precursors or other chemicals when chemicallysynthesized. A nucleic acid molecule that is substantially free ofcellular material includes preparations having less than about 30%, 20%,10%, or 5% of heterologous nucleic acid (also referred to herein as a“contaminating nucleic acid”).

A nucleic acid molecule of the present invention can be isolated usingstandard molecular biology techniques and the sequence information inthe database records described herein. Using all or a portion of suchnucleic acid sequences, nucleic acid molecules of the invention can beisolated using standard hybridization and cloning techniques (e.g., asdescribed in Sambrook et al., ed., Molecular Cloning: A LaboratoryManual, 2nd ed., Cold Spring Harbor Laboratory Press, Cold SpringHarbor, N.Y., 1989).

A nucleic acid molecule of the invention can be amplified using cDNA,mRNA, or genomic DNA as a template and appropriate oligonucleotideprimers according to standard PCR amplification techniques. The nucleicacid so amplified can be cloned into an appropriate vector andcharacterized by DNA sequence analysis. Furthermore, nucleotidescorresponding to all or a portion of a nucleic acid molecule of theinvention can be prepared by standard synthetic techniques, e.g., usingan automated DNA synthesizer.

In another preferred embodiment, an isolated nucleic acid molecule ofthe invention comprises a nucleic acid molecule which has a nucleotidesequence complementary to the nucleotide sequence of a marker nucleicacid or to the nucleotide sequence of a nucleic acid encoding a markerprotein. A nucleic acid molecule which is complementary to a givennucleotide sequence is one which is sufficiently complementary to thegiven nucleotide sequence that it can hybridize to the given nucleotidesequence thereby forming a stable duplex.

Moreover, a nucleic acid molecule of the invention can comprise only aportion of a nucleic acid sequence, wherein the full length nucleic acidsequence comprises a marker nucleic acid or which encodes a markerprotein. Such nucleic acids can be used, for example, as a probe orprimer. The probe/primer typically is used as one or more substantiallypurified oligonucleotides. The oligonucleotide typically comprises aregion of nucleotide sequence that hybridizes under stringent conditionsto at least about 7, preferably about 15, more preferably about 25, 50,75, 100, 125, 150, 175, 200, 250, 300, 350, or 400 or more consecutivenucleotides of a nucleic acid of the invention.

Probes based on the sequence of a nucleic acid molecule of the inventioncan be used to detect transcripts or genomic sequences corresponding toone or more markers of the invention. The probe comprises a label groupattached thereto, e.g., a radioisotope, a fluorescent compound, anenzyme, or an enzyme co-factor. Such probes can be used as part of adiagnostic test kit for identifying cells or tissues which mis-expressthe protein, such as by measuring levels of a nucleic acid moleculeencoding the protein in a sample of cells from a subject, e.g.,detecting mRNA levels or determining whether a gene encoding the proteinhas been mutated or deleted.

The invention further encompasses nucleic acid molecules that differ,due to degeneracy of the genetic code, from the nucleotide sequence ofnucleic acids encoding a marker protein, and thus encode the sameprotein.

It will be appreciated by those skilled in the art that DNA sequencepolymorphisms that lead to changes in the amino acid sequence can existwithin a population (e.g., the human population). Such geneticpolymorphisms can exist among individuals within a population due tonatural allelic variation. An allele is one of a group of genes whichoccur alternatively at a given genetic locus. In addition, it will beappreciated that DNA polymorphisms that affect RNA expression levels canalso exist that may affect the overall expression level of that gene(e.g., by affecting regulation or degradation).

As used herein, the phrase “allelic variant” refers to a nucleotidesequence which occurs at a given locus or to a polypeptide encoded bythe nucleotide sequence.

As used herein, the terms “gene” and “recombinant gene” refer to nucleicacid molecules comprising an open reading frame encoding a polypeptidecorresponding to a marker of the invention. Such natural allelicvariations can typically result in 1-5% variance in the nucleotidesequence of a given gene. Alternative alleles can be identified bysequencing the gene of interest in a number of different individuals.This can be readily carried out by using hybridization probes toidentify the same genetic locus in a variety of individuals. Any and allsuch nucleotide variations and resulting amino acid polymorphisms orvariations that are the result of natural allelic variation and that donot alter the functional activity are intended to be within the scope ofthe invention.

In another embodiment, an isolated nucleic acid molecule of theinvention is at least 7, 15, 20, 25, 30, 40, 60, 80, 100, 150, 200, 250,300, 350, 400, 450, 550, 650, 700, 800, 900, 1000, 1200, 1400, 1600,1800, 2000, 2200, 2400, 2600, 2800, 3000, 3500, 4000, 4500, or morenucleotides in length and hybridizes under stringent conditions to amarker nucleic acid or to a nucleic acid encoding a marker protein. Asused herein, the term “hybridizes under stringent conditions” isintended to describe conditions for hybridization and washing underwhich nucleotide sequences at least 60% (65%, 70%, preferably 75%)identical to each other typically remain hybridized to each other. Suchstringent conditions are known to those skilled in the art and can befound in sections 6.3.1-6.3.6 of Current Protocols in Molecular Biology,John Wiley & Sons, N.Y. (1989). A preferred, non-limiting example ofstringent hybridization conditions are hybridization in 6× sodiumchloride/sodium citrate (SSC) at about 45° C., followed by one or morewashes in 0.2×SSC, 0.1% SDS at 50-65° C.

In addition to naturally-occurring allelic variants of a nucleic acidmolecule of the invention that can exist in the population, the skilledartisan will further appreciate that sequence changes can be introducedby mutation thereby leading to changes in the amino acid sequence of theencoded protein, without altering the biological activity of the proteinencoded thereby. For example, one can make nucleotide substitutionsleading to amino acid substitutions at “non-essential” amino acidresidues. A “non-essential” amino acid residue is a residue that can bealtered from the wild-type sequence without altering the biologicalactivity, whereas an “essential” amino acid residue is required forbiological activity. For example, amino acid residues that are notconserved or only semi-conserved among homologs of various species maybe non-essential for activity and thus would be likely targets foralteration. Alternatively, amino acid residues that are conserved amongthe homologs of various species (e.g., murine and human) may beessential for activity and thus would not be likely targets foralteration.

Accordingly, another aspect of the invention pertains to nucleic acidmolecules encoding a variant marker protein that contain changes inamino acid residues that are not essential for activity. Such variantmarker proteins differ in amino acid sequence from thenaturally-occurring marker proteins, yet retain biological activity. Inone embodiment, such a variant marker protein has an amino acid sequencethat is at least about 40% identical, 50%, 60%, 70%, 80%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98% or 99% identical to the amino acid sequenceof a marker protein.

An isolated nucleic acid molecule encoding a variant marker protein canbe created by introducing one or more nucleotide substitutions,additions or deletions into the nucleotide sequence of marker nucleicacids, such that one or more amino acid residue substitutions,additions, or deletions are introduced into the encoded protein.Mutations can be introduced by standard techniques, such assite-directed mutagenesis and PCR-mediated mutagenesis. Preferably,conservative amino acid substitutions are made at one or more predictednon-essential amino acid residues. A “conservative amino acidsubstitution” is one in which the amino acid residue is replaced with anamino acid residue having a similar side chain. Families of amino acidresidues having similar side chains have been defined in the art. Thesefamilies include amino acids with basic side chains (e.g., lysine,arginine, histidine), acidic side chains (e.g., aspartic acid, glutamicacid), uncharged polar side chains (e.g., glycine, asparagine,glutamine, serine, threonine, tyrosine, cysteine), non-polar side chains(e.g., alanine, valine, leucine, isoleucine, proline, phenylalanine,methionine, tryptophan), beta-branched side chains (e.g., threonine,valine, isoleucine) and aromatic side chains (e.g., tyrosine,phenylalanine, tryptophan, histidine). Alternatively, mutations can beintroduced randomly along all or part of the coding sequence, such as bysaturation mutagenesis, and the resultant mutants can be screened forbiological activity to identify mutants that retain activity. Followingmutagenesis, the encoded protein can be expressed recombinantly and theactivity of the protein can be determined.

The present invention encompasses antisense nucleic acid molecules,i.e., molecules which are complementary to a sense nucleic acid of theinvention, e.g., complementary to the coding strand of a double-strandedmarker cDNA molecule or complementary to a marker mRNA sequence.Accordingly, an antisense nucleic acid of the invention can hydrogenbond to (i.e. anneal with) a sense nucleic acid of the invention. Theantisense nucleic acid can be complementary to an entire coding strand,or to only a portion thereof, e.g., all or part of the protein codingregion (or open reading frame). An antisense nucleic acid molecule canalso be antisense to all or part of a non-coding region of the codingstrand of a nucleotide sequence encoding a marker protein. Thenon-coding regions (“5′ and 3′ untranslated regions”) are the 5′ and 3′sequences which flank the coding region and are not translated intoamino acids.

An antisense oligonucleotide can be, for example, about 5, 10, 15, 20,25, 30, 35, 40, 45, or 50 or more nucleotides in length. An antisensenucleic acid of the invention can be constructed using chemicalsynthesis and enzymatic ligation reactions using procedures known in theart. For example, an antisense nucleic acid (e.g., an antisenseoligonucleotide) can be chemically synthesized using naturally occurringnucleotides or variously modified nucleotides designed to increase thebiological stability of the molecules or to increase the physicalstability of the duplex formed between the antisense and sense nucleicacids, e.g., phosphorothioate derivatives and acridine substitutednucleotides can be used. Examples of modified nucleotides which can beused to generate the antisense nucleic acid include 5-fluorouracil,5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine,4-acetylcytosine, 5-(carboxyhydroxylmethyl) uracil,5-carboxymethylaminomethyl-2-thiouridine,5-carboxymethylaminomethyluracil, dihydrouracil,beta-D-galactosylqueosine, inosine, N6-isopentenyladenine,1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine,2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-adenine,7-methylguanine, 5-methylaminomethyluracil,5-methoxyaminomethyl-2-thiouracil, beta-D-mannosylqueosine,5′-methoxycarboxymethyluracil, 5-methoxyuracil,2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v),wybutoxosine, pseudouracil, queosine, 2-thiocytosine,5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil,uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid (v),5-methyl-2-thiouracil, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w,and 2,6-diaminopurine. Alternatively, the antisense nucleic acid can beproduced biologically using an expression vector into which a nucleicacid has been sub-cloned in an antisense orientation (i.e., RNAtranscribed from the inserted nucleic acid will be of an antisenseorientation to a target nucleic acid of interest, described further inthe following subsection).

The antisense nucleic acid molecules of the invention are typicallyadministered to a subject or generated in situ such that they hybridizewith or bind to cellular mRNA and/or genomic DNA encoding a markerprotein to thereby inhibit expression of the marker, e.g., by inhibitingtranscription and/or translation. The hybridization can be byconventional nucleotide complementarity to form a stable duplex, or, forexample, in the case of an antisense nucleic acid molecule which bindsto DNA duplexes, through specific interactions in the major groove ofthe double helix. Examples of a route of administration of antisensenucleic acid molecules of the invention includes direct injection at atissue site or infusion of the antisense nucleic acid into disease stateor toxicity state associated body fluid. Alternatively, antisensenucleic acid molecules can be modified to target selected cells and thenadministered systemically. For example, for systemic administration,antisense molecules can be modified such that they specifically bind toreceptors or antigens expressed on a selected cell surface, e.g., bylinking the antisense nucleic acid molecules to peptides or antibodieswhich bind to cell surface receptors or antigens. The antisense nucleicacid molecules can also be delivered to cells using the vectorsdescribed herein. To achieve sufficient intracellular concentrations ofthe antisense molecules, vector constructs in which the antisensenucleic acid molecule is placed under the control of a strong pol II orpol III promoter are preferred.

An antisense nucleic acid molecule of the invention can be an α-anomericnucleic acid molecule. An α-anomeric nucleic acid molecule formsspecific double-stranded hybrids with complementary RNA in which,contrary to the usual α-units, the strands run parallel to each other(Gaultier et al., 1987, Nucleic Acids Res. 15:6625-6641). The antisensenucleic acid molecule can also comprise a 2′-o-methylribonucleotide(Inoue et al., 1987, Nucleic Acids Res. 15:6131-6148) or a chimericRNA-DNA analogue (Inoue et al., 1987, FEBS Lett. 215:327-330).

The invention also encompasses ribozymes. Ribozymes are catalytic RNAmolecules with ribonuclease activity which are capable of cleaving asingle-stranded nucleic acid, such as an mRNA, to which they have acomplementary region. Thus, ribozymes (e.g., hammerhead ribozymes asdescribed in Haselhoff and Gerlach, 1988, Nature 334:585-591) can beused to catalytically cleave mRNA transcripts to thereby inhibittranslation of the protein encoded by the mRNA. A ribozyme havingspecificity for a nucleic acid molecule encoding a marker protein can bedesigned based upon the nucleotide sequence of a cDNA corresponding tothe marker. For example, a derivative of a Tetrahymena L-19 IVS RNA canbe constructed in which the nucleotide sequence of the active site iscomplementary to the nucleotide sequence to be cleaved (see Cech et al.U.S. Pat. No. 4,987,071; and Cech et al. U.S. Pat. No. 5,116,742).Alternatively, an mRNA encoding a polypeptide of the invention can beused to select a catalytic RNA having a specific ribonuclease activityfrom a pool of RNA molecules (see, e.g., Bartel and Szostak, 1993,Science 261:1411-1418).

The invention also encompasses nucleic acid molecules which form triplehelical structures. For example, expression of a marker of the inventioncan be inhibited by targeting nucleotide sequences complementary to theregulatory region of the gene encoding the marker nucleic acid orprotein (e.g., the promoter and/or enhancer) to form triple helicalstructures that prevent transcription of the gene in target cells. Seegenerally Helene (1991) Anticancer Drug Des. 6(6):569-84; Helene (1992)Ann. N.Y. Acad. Sci. 660:27-36; and Maher (1992) Bioassays14(12):807-15.

In various embodiments, the nucleic acid molecules of the invention canbe modified at the base moiety, sugar moiety or phosphate backbone toimprove, e.g., the stability, hybridization, or solubility of themolecule. For example, the deoxyribose phosphate backbone of the nucleicacids can be modified to generate peptide nucleic acids (see Hyrup etal., 1996, Bioorganic & Medicinal Chemistry 4(1): 5-23). As used herein,the terms “peptide nucleic acids” or “PNAs” refer to nucleic acidmimics, e.g., DNA mimics, in which the deoxyribose phosphate backbone isreplaced by a pseudopeptide backbone and only the four naturalnucleobases are retained. The neutral backbone of PNAs has been shown toallow for specific hybridization to DNA and RNA under conditions of lowionic strength. The synthesis of PNA oligomers can be performed usingstandard solid phase peptide synthesis protocols as described in Hyrupet al. (1996), supra; Perry-O'Keefe et al. (1996) Proc. Natl. Acad. Sci.USA 93:14670-675.

PNAs can be used in therapeutic and diagnostic applications. Forexample, PNAs can be used as antisense or antigene agents forsequence-specific modulation of gene expression by, e.g., inducingtranscription or translation arrest or inhibiting replication. PNAs canalso be used, e.g., in the analysis of single base pair mutations in agene by, e.g., PNA directed PCR clamping; as artificial restrictionenzymes when used in combination with other enzymes, e.g., S1 nucleases(Hyrup (1996), supra; or as probes or primers for DNA sequence andhybridization (Hyrup, 1996, supra; Perry-O'Keefe et al., 1996, Proc.Natl. Acad. Sci. USA 93:14670-675).

In another embodiment, PNAs can be modified, e.g., to enhance theirstability or cellular uptake, by attaching lipophilic or other helpergroups to PNA, by the formation of PNA-DNA chimeras, or by the use ofliposomes or other techniques of drug delivery known in the art. Forexample, PNA-DNA chimeras can be generated which can combine theadvantageous properties of PNA and DNA. Such chimeras allow DNArecognition enzymes, e.g., RNase H and DNA polymerases, to interact withthe DNA portion while the PNA portion would provide high bindingaffinity and specificity. PNA-DNA chimeras can be linked using linkersof appropriate lengths selected in terms of base stacking, number ofbonds between the nucleobases, and orientation (Hyrup, 1996, supra). Thesynthesis of PNA-DNA chimeras can be performed as described in Hyrup(1996), supra, and Finn et al. (1996) Nucleic Acids Res. 24(17):3357-63.For example, a DNA chain can be synthesized on a solid support usingstandard phosphoramidite coupling chemistry and modified nucleosideanalogs. Compounds such as 5′-(4-methoxytrityl)amino-5′-deoxy-thymidinephosphoramidite can be used as a link between the PNA and the 5′ end ofDNA (Mag et al., 1989, Nucleic Acids Res. 17:5973-88). PNA monomers arethen coupled in a step-wise manner to produce a chimeric molecule with a5′ PNA segment and a 3′ DNA segment (Finn et al., 1996, Nucleic AcidsRes. 24(17):3357-63). Alternatively, chimeric molecules can besynthesized with a 5′ DNA segment and a 3′ PNA segment (Peterser et al.,1975, Bioorganic Med. Chem. Lett. 5:1119-11124).

In other embodiments, the oligonucleotide can include other appendedgroups such as peptides (e.g., for targeting host cell receptors invivo), or agents facilitating transport across the cell membrane (see,e.g., Letsinger et al., 1989, Proc. Natl. Acad. Sci. USA 86:6553-6556;Lemaitre et al., 1987, Proc. Natl. Acad. Sci. USA 84:648-652; PCTPublication No. WO 88/09810) or the blood-brain barrier (see, e.g., PCTPublication No. WO 89/10134). In addition, oligonucleotides can bemodified with hybridization-triggered cleavage agents (see, e.g., Krolet al., 1988, Bio/Techniques 6:958-976) or intercalating agents (see,e.g., Zon, 1988, Pharm. Res. 5:539-549). To this end, theoligonucleotide can be conjugated to another molecule, e.g., a peptide,hybridization triggered cross-linking agent, transport agent,hybridization-triggered cleavage agent, etc.

The invention also includes molecular beacon nucleic acids having atleast one region which is complementary to a nucleic acid of theinvention, such that the molecular beacon is useful for quantitating thepresence of the nucleic acid of the invention in a sample. A “molecularbeacon” nucleic acid is a nucleic acid comprising a pair ofcomplementary regions and having a fluorophore and a fluorescentquencher associated therewith. The fluorophore and quencher areassociated with different portions of the nucleic acid in such anorientation that when the complementary regions are annealed with oneanother, fluorescence of the fluorophore is quenched by the quencher.When the complementary regions of the nucleic acid are not annealed withone another, fluorescence of the fluorophore is quenched to a lesserdegree. Molecular beacon nucleic acids are described, for example, inU.S. Pat. No. 5,876,930.

D. Isolated Proteins and Antibodies

One aspect of the invention pertains to isolated marker proteins andbiologically active portions thereof, as well as polypeptide fragmentssuitable for use as immunogens to raise antibodies directed against amarker protein or a fragment thereof. In one embodiment, the nativemarker protein can be isolated from cells or tissue sources by anappropriate purification scheme using standard protein purificationtechniques. In another embodiment, a protein or peptide comprising thewhole or a segment of the marker protein is produced by recombinant DNAtechniques. Alternative to recombinant expression, such protein orpeptide can be synthesized chemically using standard peptide synthesistechniques.

An “isolated” or “purified” protein or biologically active portionthereof is substantially free of cellular material or othercontaminating proteins from the cell or tissue source from which theprotein is derived, or substantially free of chemical precursors orother chemicals when chemically synthesized. The language “substantiallyfree of cellular material” includes preparations of protein in which theprotein is separated from cellular components of the cells from which itis isolated or recombinantly produced. Thus, protein that issubstantially free of cellular material includes preparations of proteinhaving less than about 30%, 20%, 10%, or 5% (by dry weight) ofheterologous protein (also referred to herein as a “contaminatingprotein”). When the protein or biologically active portion thereof isrecombinantly produced, it is also preferably substantially free ofculture medium, i.e., culture medium represents less than about 20%,10%, or 5% of the volume of the protein preparation. When the protein isproduced by chemical synthesis, it is preferably substantially free ofchemical precursors or other chemicals, i.e., it is separated fromchemical precursors or other chemicals which are involved in thesynthesis of the protein. Accordingly such preparations of the proteinhave less than about 30%, 20%, 10%, 5% (by dry weight) of chemicalprecursors or compounds other than the polypeptide of interest.

Biologically active portions of a marker protein include polypeptidescomprising amino acid sequences sufficiently identical to or derivedfrom the amino acid sequence of the marker protein, which include feweramino acids than the full length protein, and exhibit at least oneactivity of the corresponding full-length protein. Typically,biologically active portions comprise a domain or motif with at leastone activity of the corresponding full-length protein. A biologicallyactive portion of a marker protein of the invention can be a polypeptidewhich is, for example, 10, 25, 50, 100 or more amino acids in length.Moreover, other biologically active portions, in which other regions ofthe marker protein are deleted, can be prepared by recombinanttechniques and evaluated for one or more of the functional activities ofthe native form of the marker protein.

Preferred marker proteins are encoded by nucleotide sequences comprisingthe sequences encoding any of the genes described in the examples. Otheruseful proteins are substantially identical (e.g., at least about 40%,preferably 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98% or 99%) to one of these sequences and retain the functional activityof the corresponding naturally-occurring marker protein yet differ inamino acid sequence due to natural allelic variation or mutagenesis.

To determine the percent identity of two amino acid sequences or of twonucleic acids, the sequences are aligned for optimal comparison purposes(e.g., gaps can be introduced in the sequence of a first amino acid ornucleic acid sequence for optimal alignment with a second amino ornucleic acid sequence). The amino acid residues or nucleotides atcorresponding amino acid positions or nucleotide positions are thencompared. When a position in the first sequence is occupied by the sameamino acid residue or nucleotide as the corresponding position in thesecond sequence, then the molecules are identical at that position.Preferably, the percent identity between the two sequences is calculatedusing a global alignment. Alternatively, the percent identity betweenthe two sequences is calculated using a local alignment. The percentidentity between the two sequences is a function of the number ofidentical positions shared by the sequences (i.e., % identity=# ofidentical positions/total # of positions (e.g., overlappingpositions)×100). In one embodiment the two sequences are the samelength. In another embodiment, the two sequences are not the samelength.

The determination of percent identity between two sequences can beaccomplished using a mathematical algorithm. A preferred, non-limitingexample of a mathematical algorithm utilized for the comparison of twosequences is the algorithm of Karlin and Altschul (1990) Proc. Natl.Acad. Sci. USA 87:2264-2268, modified as in Karlin and Altschul (1993)Proc. Natl. Acad. Sci. USA 90:5873-5877. Such an algorithm isincorporated into the BLASTN and BLASTX programs of Altschul, et al.(1990) J. Mol. Biol. 215:403-410. BLAST nucleotide searches can beperformed with the BLASTN program, score=100, wordlength=12 to obtainnucleotide sequences homologous to a nucleic acid molecules of theinvention. BLAST protein searches can be performed with the BLASTPprogram, score=50, wordlength=3 to obtain amino acid sequenceshomologous to a protein molecules of the invention. To obtain gappedalignments for comparison purposes, a newer version of the BLASTalgorithm called Gapped BLAST can be utilized as described in Altschulet al. (1997) Nucleic Acids Res. 25:3389-3402, which is able to performgapped local alignments for the programs BLASTN, BLASTP and BLASTX.Alternatively, PSI-Blast can be used to perform an iterated search whichdetects distant relationships between molecules. When utilizing BLAST,Gapped BLAST, and PSI-Blast programs, the default parameters of therespective programs (e.g., BLASTX and BLASTN) can be used. Seewww.ncbi.nlm.nih.gov. Another preferred, non-limiting example of amathematical algorithm utilized for the comparison of sequences is thealgorithm of Myers and Miller, (1988) CABIOS 4:11-17. Such an algorithmis incorporated into the ALIGN program (version 2.0) which is part ofthe GCG sequence alignment software package. When utilizing the ALIGNprogram for comparing amino acid sequences, a PAM120 weight residuetable, a gap length penalty of 12, and a gap penalty of 4 can be used.Yet another useful algorithm for identifying regions of local sequencesimilarity and alignment is the FASTA algorithm as described in Pearsonand Lipman (1988) Proc. Natl. Acad. Sci. USA 85:2444-2448. When usingthe FASTA algorithm for comparing nucleotide or amino acid sequences, aPAM120 weight residue table can, for example, be used with a k-tuplevalue of 2.

The percent identity between two sequences can be determined usingtechniques similar to those described above, with or without allowinggaps. In calculating percent identity, only exact matches are counted.

The invention also provides chimeric or fusion proteins comprising amarker protein or a segment thereof. As used herein, a “chimericprotein” or “fusion protein” comprises all or part (preferably abiologically active part) of a marker protein operably linked to aheterologous polypeptide (i.e., a polypeptide other than the markerprotein). Within the fusion protein, the term “operably linked” isintended to indicate that the marker protein or segment thereof and theheterologous polypeptide are fused in-frame to each other. Theheterologous polypeptide can be fused to the amino-terminus or thecarboxyl-terminus of the marker protein or segment.

One useful fusion protein is a GST fusion protein in which a markerprotein or segment is fused to the carboxyl terminus of GST sequences.Such fusion proteins can facilitate the purification of a recombinantpolypeptide of the invention.

In another embodiment, the fusion protein contains a heterologous signalsequence at its amino terminus. For example, the native signal sequenceof a marker protein can be removed and replaced with a signal sequencefrom another protein. For example, the gp67 secretory sequence of thebaculovirus envelope protein can be used as a heterologous signalsequence (Ausubel et al., ed., Current Protocols in Molecular Biology,John Wiley & Sons, NY, 1992). Other examples of eukaryotic heterologoussignal sequences include the secretory sequences of melittin and humanplacental alkaline phosphatase (Stratagene; La Jolla, Calif.). In yetanother example, useful prokaryotic heterologous signal sequencesinclude the phoA secretory signal (Sambrook et al., supra) and theprotein A secretory signal (Pharmacia Biotech; Piscataway, N.J.).

In yet another embodiment, the fusion protein is an immunoglobulinfusion protein in which all or part of a marker protein is fused tosequences derived from a member of the immunoglobulin protein family.The immunoglobulin fusion proteins of the invention can be incorporatedinto pharmaceutical compositions and administered to a subject toinhibit an interaction between a ligand (soluble or membrane-bound) anda protein on the surface of a cell (receptor), to thereby suppresssignal transduction in vivo. The immunoglobulin fusion protein can beused to affect the bioavailability of a cognate ligand of a markerprotein. Inhibition of ligand/receptor interaction can be usefultherapeutically, both for treating proliferative and differentiativedisorders and for modulating (e.g. promoting or inhibiting) cellsurvival. Moreover, the immunoglobulin fusion proteins of the inventioncan be used as immunogens to produce antibodies directed against amarker protein in a subject, to purify ligands and in screening assaysto identify molecules which inhibit the interaction of the markerprotein with ligands.

Chimeric and fusion proteins of the invention can be produced bystandard recombinant DNA techniques. In another embodiment, the fusiongene can be synthesized by conventional techniques including automatedDNA synthesizers. Alternatively, PCR amplification of gene fragments canbe carried out using anchor primers which give rise to complementaryoverhangs between two consecutive gene fragments which can subsequentlybe annealed and re-amplified to generate a chimeric gene sequence (see,e.g., Ausubel et al., supra). Moreover, many expression vectors arecommercially available that already encode a fusion moiety (e.g., a GSTpolypeptide). A nucleic acid encoding a polypeptide of the invention canbe cloned into such an expression vector such that the fusion moiety islinked in-frame to the polypeptide of the invention.

A signal sequence can be used to facilitate secretion and isolation ofmarker proteins. Signal sequences are typically characterized by a coreof hydrophobic amino acids which are generally cleaved from the matureprotein during secretion in one or more cleavage events. Such signalpeptides contain processing sites that allow cleavage of the signalsequence from the mature proteins as they pass through the secretorypathway. Thus, the invention pertains to marker proteins, fusionproteins or segments thereof having a signal sequence, as well as tosuch proteins from which the signal sequence has been proteolyticallycleaved (i.e., the cleavage products). In one embodiment, a nucleic acidsequence encoding a signal sequence can be operably linked in anexpression vector to a protein of interest, such as a marker protein ora segment thereof. The signal sequence directs secretion of the protein,such as from a eukaryotic host into which the expression vector istransformed, and the signal sequence is subsequently or concurrentlycleaved. The protein can then be readily purified from the extracellularmedium by art recognized methods. Alternatively, the signal sequence canbe linked to the protein of interest using a sequence which facilitatespurification, such as with a GST domain.

The present invention also pertains to variants of the marker proteins.Such variants have an altered amino acid sequence which can function aseither agonists (mimetics) or as antagonists. Variants can be generatedby mutagenesis, e.g., discrete point mutation or truncation. An agonistcan retain substantially the same, or a subset, of the biologicalactivities of the naturally occurring form of the protein. An antagonistof a protein can inhibit one or more of the activities of the naturallyoccurring form of the protein by, for example, competitively binding toa downstream or upstream member of a cellular signaling cascade whichincludes the protein of interest. Thus, specific biological effects canbe elicited by treatment with a variant of limited function. Treatmentof a subject with a variant having a subset of the biological activitiesof the naturally occurring form of the protein can have fewer sideeffects in a subject relative to treatment with the naturally occurringform of the protein.

Variants of a marker protein which function as either agonists(mimetics) or as antagonists can be identified by screeningcombinatorial libraries of mutants, e.g., truncation mutants, of theprotein of the invention for agonist or antagonist activity. In oneembodiment, a variegated library of variants is generated bycombinatorial mutagenesis at the nucleic acid level and is encoded by avariegated gene library. A variegated library of variants can beproduced by, for example, enzymatically ligating a mixture of syntheticoligonucleotides into gene sequences such that a degenerate set ofpotential protein sequences is expressible as individual polypeptides,or alternatively, as a set of larger fusion proteins (e.g., for phagedisplay). There are a variety of methods which can be used to producelibraries of potential variants of the marker proteins from a degenerateoligonucleotide sequence. Methods for synthesizing degenerateoligonucleotides are known in the art (see, e.g., Narang, 1983,Tetrahedron 39:3; Itakura et al., 1984, Annu. Rev. Biochem. 53:323;Itakura et al., 1984, Science 198:1056; Ike et al., 1983 Nucleic AcidRes. 11:477).

In addition, libraries of segments of a marker protein can be used togenerate a variegated population of polypeptides for screening andsubsequent selection of variant marker proteins or segments thereof. Forexample, a library of coding sequence fragments can be generated bytreating a double stranded PCR fragment of the coding sequence ofinterest with a nuclease under conditions wherein nicking occurs onlyabout once per molecule, denaturing the double stranded DNA, renaturingthe DNA to form double stranded DNA which can include sense/antisensepairs from different nicked products, removing single stranded portionsfrom reformed duplexes by treatment with S1 nuclease, and ligating theresulting fragment library into an expression vector. By this method, anexpression library can be derived which encodes amino terminal andinternal fragments of various sizes of the protein of interest.

Several techniques are known in the art for screening gene products ofcombinatorial libraries made by point mutations or truncation, and forscreening cDNA libraries for gene products having a selected property.The most widely used techniques, which are amenable to high through-putanalysis, for screening large gene libraries typically include cloningthe gene library into replicable expression vectors, transformingappropriate cells with the resulting library of vectors, and expressingthe combinatorial genes under conditions in which detection of a desiredactivity facilitates isolation of the vector encoding the gene whoseproduct was detected. Recursive ensemble mutagenesis (REM), a techniquewhich enhances the frequency of functional mutants in the libraries, canbe used in combination with the screening assays to identify variants ofa protein of the invention (Arkin and Yourvan, 1992, Proc. Natl. Acad.Sci. USA 89:7811-7815; Delgrave et al., 1993, Protein Engineering6(3):327-331).

Another aspect of the invention pertains to antibodies directed againsta protein of the invention. In preferred embodiments, the antibodiesspecifically bind a marker protein or a fragment thereof. The terms“antibody” and “antibodies” as used interchangeably herein refer toimmunoglobulin molecules as well as fragments and derivatives thereofthat comprise an immunologically active portion of an immunoglobulinmolecule, (i.e., such a portion contains an antigen binding site whichspecifically binds an antigen, such as a marker protein, e.g., anepitope of a marker protein). An antibody which specifically binds to aprotein of the invention is an antibody which binds the protein, butdoes not substantially bind other molecules in a sample, e.g., abiological sample, which naturally contains the protein. Examples of animmunologically active portion of an immunoglobulin molecule include,but are not limited to, single-chain antibodies (scAb), F(ab) andF(ab′)₂ fragments.

An isolated protein of the invention or a fragment thereof can be usedas an immunogen to generate antibodies. The full-length protein can beused or, alternatively, the invention provides antigenic peptidefragments for use as immunogens. The antigenic peptide of a protein ofthe invention comprises at least 8 (preferably 10, 15, 20, or 30 ormore) amino acid residues of the amino acid sequence of one of theproteins of the invention, and encompasses at least one epitope of theprotein such that an antibody raised against the peptide forms aspecific immune complex with the protein. Preferred epitopes encompassedby the antigenic peptide are regions that are located on the surface ofthe protein, e.g., hydrophilic regions. Hydrophobicity sequenceanalysis, hydrophilicity sequence analysis, or similar analyses can beused to identify hydrophilic regions. In preferred embodiments, anisolated marker protein or fragment thereof is used as an immunogen.

An immunogen typically is used to prepare antibodies by immunizing asuitable (i.e. immunocompetent) subject such as a rabbit, goat, mouse,or other mammal or vertebrate. An appropriate immunogenic preparationcan contain, for example, recombinantly-expressed orchemically-synthesized protein or peptide. The preparation can furtherinclude an adjuvant, such as Freund's complete or incomplete adjuvant,or a similar immunostimulatory agent. Preferred immunogen compositionsare those that contain no other human proteins such as, for example,immunogen compositions made using a non-human host cell for recombinantexpression of a protein of the invention. In such a manner, theresulting antibody compositions have reduced or no binding of humanproteins other than a protein of the invention.

The invention provides polyclonal and monoclonal antibodies. The term“monoclonal antibody” or “monoclonal antibody composition”, as usedherein, refers to a population of antibody molecules that contain onlyone species of an antigen binding site capable of immunoreacting with aparticular epitope. Preferred polyclonal and monoclonal antibodycompositions are ones that have been selected for antibodies directedagainst a protein of the invention. Particularly preferred polyclonaland monoclonal antibody preparations are ones that contain onlyantibodies directed against a marker protein or fragment thereof.

Polyclonal antibodies can be prepared by immunizing a suitable subjectwith a protein of the invention as an immunogen. The antibody titer inthe immunized subject can be monitored over time by standard techniques,such as with an enzyme linked immunosorbent assay (ELISA) usingimmobilized polypeptide. At an appropriate time after immunization,e.g., when the specific antibody titers are highest, antibody-producingcells can be obtained from the subject and used to prepare monoclonalantibodies (mAb) by standard techniques, such as the hybridoma techniqueoriginally described by Kohler and Milstein (1975) Nature 256:495-497,the human B cell hybridoma technique (see Kozbor et al., 1983, Immunol.Today 4:72), the EBV-hybridoma technique (see Cole et al., pp. 77-96 InMonoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc., 1985) ortrioma techniques. The technology for producing hybridomas is well known(see generally Current Protocols in Immunology, Coligan et al. ed., JohnWiley & Sons, New York, 1994). Hybridoma cells producing a monoclonalantibody of the invention are detected by screening the hybridomaculture supernatants for antibodies that bind the polypeptide ofinterest, e.g., using a standard ELISA assay.

Alternative to preparing monoclonal antibody-secreting hybridomas, amonoclonal antibody directed against a protein of the invention can beidentified and isolated by screening a recombinant combinatorialimmunoglobulin library (e.g., an antibody phage display library) withthe polypeptide of interest. Kits for generating and screening phagedisplay libraries are commercially available (e.g., the PharmaciaRecombinant Phage Antibody System, Catalog No. 27-9400-01; and theStratagene SurfZAP Phage Display Kit, Catalog No. 240612). Additionally,examples of methods and reagents particularly amenable for use ingenerating and screening antibody display library can be found in, forexample, U.S. Pat. No. 5,223,409; PCT Publication No. WO 92/18619; PCTPublication No. WO 91/17271; PCT Publication No. WO 92/20791; PCTPublication No. WO 92/15679; PCT Publication No. WO 93/01288; PCTPublication No. WO 92/01047; PCT Publication No. WO 92/09690; PCTPublication No. WO 90/02809; Fuchs et al. (1991) Bio/Technology9:1370-1372; Hay et al. (1992) Hum. Antibod. Hybridomas 3:81-85; Huse etal. (1989) Science 246:1275-1281; Griffiths et al. (1993) EMBO J.12:725-734.

The invention also provides recombinant antibodies that specificallybind a protein of the invention. In preferred embodiments, therecombinant antibodies specifically binds a marker protein or fragmentthereof. Recombinant antibodies include, but are not limited to,chimeric and humanized monoclonal antibodies, comprising both human andnon-human portions, single-chain antibodies and multi-specificantibodies. A chimeric antibody is a molecule in which differentportions are derived from different animal species, such as those havinga variable region derived from a murine mAb and a human immunoglobulinconstant region. (See, e.g., Cabilly et al., U.S. Pat. No. 4,816,567;and Boss et al., U.S. Pat. No. 4,816,397, which are incorporated hereinby reference in their entirety.) Single-chain antibodies have an antigenbinding site and consist of a single polypeptide. They can be producedby techniques known in the art, for example using methods described inLadner et. al U.S. Pat. No. 4,946,778 (which is incorporated herein byreference in its entirety); Bird et al., (1988) Science 242:423-426;Whitlow et al., (1991) Methods in Enzymology 2:1-9; Whitlow et al.,(1991) Methods in Enzymology 2:97-105; and Huston et al., (1991) Methodsin Enzymology Molecular Design and Modeling: Concepts and Applications203:46-88. Multi-specific antibodies are antibody molecules having atleast two antigen-binding sites that specifically bind differentantigens. Such molecules can be produced by techniques known in the art,for example using methods described in Segal, U.S. Pat. No. 4,676,980(the disclosure of which is incorporated herein by reference in itsentirety); Holliger et al., (1993) Proc. Natl. Acad. Sci. USA90:6444-6448; Whitlow et al., (1994) Protein Eng. 7:1017-1026 and U.S.Pat. No. 6,121,424.

Humanized antibodies are antibody molecules from non-human specieshaving one or more complementarity determining regions (CDRs) from thenon-human species and a framework region from a human immunoglobulinmolecule. (See, e.g., Queen, U.S. Pat. No. 5,585,089, which isincorporated herein by reference in its entirety.) Humanized monoclonalantibodies can be produced by recombinant DNA techniques known in theart, for example using methods described in PCT Publication No. WO87/02671; European Patent Application 184,187; European PatentApplication 171,496; European Patent Application 173,494; PCTPublication No. WO 86/01533; U.S. Pat. No. 4,816,567; European PatentApplication 125,023; Better et al. (1988) Science 240:1041-1043; Liu etal. (1987) Proc. Natl. Acad. Sci. USA 84:3439-3443; Liu et al. (1987) J.Immunol. 139:3521-3526; Sun et al. (1987) Proc. Natl. Acad. Sci. USA84:214-218; Nishimura et al. (1987) Cancer Res. 47:999-1005; Wood et al.(1985) Nature 314:446-449; and Shaw et al. (1988) J. Natl. Cancer Inst.80:1553-1559); Morrison (1985) Science 229:1202-1207; Oi et al. (1986)Bio/Techniques 4:214; U.S. Pat. No. 5,225,539; Jones et al. (1986)Nature 321:552-525; Verhoeyan et al. (1988) Science 239:1534; andBeidler et al. (1988) J. Immunol. 141:4053-4060.

More particularly, humanized antibodies can be produced, for example,using transgenic mice which are incapable of expressing endogenousimmunoglobulin heavy and light chains genes, but which can express humanheavy and light chain genes. The transgenic mice are immunized in thenormal fashion with a selected antigen, e.g., all or a portion of apolypeptide corresponding to a marker of the invention. Monoclonalantibodies directed against the antigen can be obtained usingconventional hybridoma technology. The human immunoglobulin transgenesharbored by the transgenic mice rearrange during B cell differentiation,and subsequently undergo class switching and somatic mutation. Thus,using such a technique, it is possible to produce therapeutically usefulIgG, IgA and IgE antibodies. For an overview of this technology forproducing human antibodies, see Lonberg and Huszar (1995) Int. Rev.Immunol. 13:65-93). For a detailed discussion of this technology forproducing human antibodies and human monoclonal antibodies and protocolsfor producing such antibodies, see, e.g., U.S. Pat. No. 5,625,126; U.S.Pat. No. 5,633,425; U.S. Pat. No. 5,569,825; U.S. Pat. No. 5,661,016;and U.S. Pat. No. 5,545,806. In addition, companies such as Abgenix,Inc. (Freemont, Calif.), can be engaged to provide human antibodiesdirected against a selected antigen using technology similar to thatdescribed above.

Completely human antibodies which recognize a selected epitope can begenerated using a technique referred to as “guided selection.” In thisapproach a selected non-human monoclonal antibody, e.g., a murineantibody, is used to guide the selection of a completely human antibodyrecognizing the same epitope (Jespers et al., 1994, Bio/technology12:899-903).

The antibodies of the invention can be isolated after production (e.g.,from the blood or serum of the subject) or synthesis and furtherpurified by well-known techniques. For example, IgG antibodies can bepurified using protein A chromatography. Antibodies specific for aprotein of the invention can be selected or (e.g., partially purified)or purified by, e.g., affinity chromatography. For example, arecombinantly expressed and purified (or partially purified) protein ofthe invention is produced as described herein, and covalently ornon-covalently coupled to a solid support such as, for example, achromatography column. The column can then be used to affinity purifyantibodies specific for the proteins of the invention from a samplecontaining antibodies directed against a large number of differentepitopes, thereby generating a substantially purified antibodycomposition, i.e., one that is substantially free of contaminatingantibodies. By a substantially purified antibody composition is meant,in this context, that the antibody sample contains at most only 30% (bydry weight) of contaminating antibodies directed against epitopes otherthan those of the desired protein of the invention, and preferably atmost 20%, yet more preferably at most 10%, and most preferably at most5% (by dry weight) of the sample is contaminating antibodies. A purifiedantibody composition means that at least 99% of the antibodies in thecomposition are directed against the desired protein of the invention.

In a preferred embodiment, the substantially purified antibodies of theinvention may specifically bind to a signal peptide, a secretedsequence, an extracellular domain, a transmembrane or a cytoplasmicdomain or cytoplasmic membrane of a protein of the invention. In aparticularly preferred embodiment, the substantially purified antibodiesof the invention specifically bind to a secreted sequence or anextracellular domain of the amino acid sequences of a protein of theinvention. In a more preferred embodiment, the substantially purifiedantibodies of the invention specifically bind to a secreted sequence oran extracellular domain of the amino acid sequences of a marker protein.

An antibody directed against a protein of the invention can be used toisolate the protein by standard techniques, such as affinitychromatography or immunoprecipitation. Moreover, such an antibody can beused to detect the marker protein or fragment thereof (e.g., in acellular lysate or cell supernatant) in order to evaluate the level andpattern of expression of the marker. The antibodies can also be useddiagnostically to monitor protein levels in tissues or body fluids (e.g.in disease state or toxicity state associated body fluid) as part of aclinical testing procedure, e.g., to, for example, determine theefficacy of a given treatment regimen. Detection can be facilitated bythe use of an antibody derivative, which comprises an antibody of theinvention coupled to a detectable substance. Examples of detectablesubstances include various enzymes, prosthetic groups, fluorescentmaterials, luminescent materials, bioluminescent materials, andradioactive materials. Examples of suitable enzymes include horseradishperoxidase, alkaline phosphatase, β-galactosidase, oracetylcholinesterase; examples of suitable prosthetic group complexesinclude streptavidin/biotin and avidin/biotin; examples of suitablefluorescent materials include umbelliferone, fluorescein, fluoresceinisothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansylchloride or phycoerythrin; an example of a luminescent material includesluminol; examples of bioluminescent materials include luciferase,luciferin, and aequorin, and examples of suitable radioactive materialinclude ¹²⁵I, ¹³¹I, ³⁵S, or ³H.

Antibodies of the invention may also be used as therapeutic agents intreating cancers. In a preferred embodiment, completely human antibodiesof the invention are used for therapeutic treatment of human cancerpatients, particularly those having a cancer. In another preferredembodiment, antibodies that bind specifically to a marker protein orfragment thereof are used for therapeutic treatment. Further, suchtherapeutic antibody may be an antibody derivative or immunotoxincomprising an antibody conjugated to a therapeutic moiety such as acytotoxin, a therapeutic agent or a radioactive metal ion. A cytotoxinor cytotoxic agent includes any agent that is detrimental to cells.Examples include taxol, cytochalasin B, gramicidin D, ethidium bromide,emetine, mitomycin, etoposide, tenoposide, vincristine, vinblastine,colchicin, doxorubicin, daunorubicin, dihydroxy anthracin dione,mitoxantrone, mithramycin, actinomycin D, 1-dehydrotestosterone,glucocorticoids, procaine, tetracaine, lidocaine, propranolol, andpuromycin and analogs or homologs thereof. Therapeutic agents include,but are not limited to, antimetabolites (e.g., methotrexate,6-mercaptopurine, 6-thioguanine, cytarabine, 5-fluorouracildecarbazine), alkylating agents (e.g., mechlorethamine, thioepachlorambucil, melphalan, carmustine (BSNU) and lomustine (CCNU),cyclothosphamide, busulfan, dibromomannitol, streptozotocin, mitomycinC, and cis-dichlorodiamine platinum (II) (DDP) cisplatin),anthracyclines (e.g., daunorubicin (formerly daunomycin) anddoxorubicin), antibiotics (e.g., dactinomycin (formerly actinomycin),bleomycin, mithramycin, and anthramycin (AMC)), and anti-mitotic agents(e.g., vincristine and vinblastine).

The conjugated antibodies of the invention can be used for modifying agiven biological response, for the drug moiety is not to be construed aslimited to classical chemical therapeutic agents. For example, the drugmoiety may be a protein or polypeptide possessing a desired biologicalactivity. Such proteins may include, for example, a toxin such asribosome-inhibiting protein (see Better et al., U.S. Pat. No. 6,146,631,the disclosure of which is incorporated herein in its entirety), abrin,ricin A, pseudomonas exotoxin, or diphtheria toxin; a protein such astumor necrosis factor, .alpha.-interferon, β-interferon, nerve growthfactor, platelet derived growth factor, tissue plasminogen activator;or, biological response modifiers such as, for example, lymphokines,interleukin-1 (“IL-1”), interleukin-2 (“IL-2”), interleukin-6 (“IL-6”),granulocyte macrophase colony stimulating factor (“GM-CSF”), granulocytecolony stimulating factor (“G-CSF”), or other growth factors.

Techniques for conjugating such therapeutic moiety to antibodies arewell known, see, e.g., Amon et al., “Monoclonal Antibodies ForImmunotargeting Of Drugs In Cancer Therapy”, in Monoclonal AntibodiesAnd Cancer Therapy, Reisfeld et al. (eds.), pp. 243-56 (Alan R. Liss,Inc. 1985); Hellstrom et al., “Antibodies For Drug Delivery”, inControlled Drug Delivery (2nd Ed.), Robinson et al. (eds.), pp. 623-53(Marcel Dekker, Inc. 1987); Thorpe, “Antibody Carriers Of CytotoxicAgents In Cancer Therapy: A Review”, in Monoclonal Antibodies '84:Biological And Clinical Applications, Pinchera et al. (eds.), pp.475-506 (1985); “Analysis, Results, And Future Prospective Of TheTherapeutic Use Of Radiolabeled Antibody In Cancer Therapy”, inMonoclonal Antibodies For Cancer Detection And Therapy, Baldwin et al.(eds.), pp. 303-16 (Academic Press 1985), and Thorpe et al., “ThePreparation And Cytotoxic Properties Of Antibody-Toxin Conjugates”,Immunol. Rev., 62:119-58 (1982).

Accordingly, in one aspect, the invention provides substantiallypurified antibodies, antibody fragments and derivatives, all of whichspecifically bind to a protein of the invention and preferably, a markerprotein. In various embodiments, the substantially purified antibodiesof the invention, or fragments or derivatives thereof, can be human,non-human, chimeric and/or humanized antibodies. In another aspect, theinvention provides non-human antibodies, antibody fragments andderivatives, all of which specifically bind to a protein of theinvention and preferably, a marker protein. Such non-human antibodiescan be goat, mouse, sheep, horse, chicken, rabbit, or rat antibodies.Alternatively, the non-human antibodies of the invention can be chimericand/or humanized antibodies. In addition, the non-human antibodies ofthe invention can be polyclonal antibodies or monoclonal antibodies. Instill a further aspect, the invention provides monoclonal antibodies,antibody fragments and derivatives, all of which specifically bind to aprotein of the invention and preferably, a marker protein. Themonoclonal antibodies can be human, humanized, chimeric and/or non-humanantibodies.

The invention also provides a kit containing an antibody of theinvention conjugated to a detectable substance, and instructions foruse. Still another aspect of the invention is a pharmaceuticalcomposition comprising an antibody of the invention. In one embodiment,the pharmaceutical composition comprises an antibody of the inventionand a pharmaceutically acceptable carrier.

E. Predictive Medicine

The present invention pertains to the field of predictive medicine inwhich diagnostic assays, prognostic assays, pharmacogenomics, andmonitoring clinical trails are used for prognostic (predictive) purposesto thereby treat an individual prophylactically. Accordingly, one aspectof the present invention relates to diagnostic assays for determiningthe level of expression of one or more marker proteins or nucleic acids,in order to determine whether an individual is at risk of developingcertain disease or drug-induced toxicity. Such assays can be used forprognostic or predictive purposes to thereby prophylactically treat anindividual prior to the onset of the disorder.

Yet another aspect of the invention pertains to monitoring the influenceof agents (e.g., drugs or other compounds administered either to inhibitor to treat or prevent a disorder or drug-induced toxicity {i.e. inorder to understand any drug-induced toxic effects that such treatmentmay have}) on the expression or activity of a marker of the invention inclinical trials. These and other agents are described in further detailin the following sections.

F. Diagnostic Assays

An exemplary method for detecting the presence or absence of a markerprotein or nucleic acid in a biological sample involves obtaining abiological sample (e.g. toxicity-associated body fluid or tissue sample)from a test subject and contacting the biological sample with a compoundor an agent capable of detecting the polypeptide or nucleic acid (e.g.,mRNA, genomic DNA, or cDNA). The detection methods of the invention canthus be used to detect mRNA, protein, cDNA, or genomic DNA, for example,in a biological sample in vitro as well as in vivo. For example, invitro techniques for detection of mRNA include Northern hybridizationsand in situ hybridizations. In vitro techniques for detection of amarker protein include enzyme linked immunosorbent assays (ELISAs),Western blots, immunoprecipitations and immunofluorescence. In vitrotechniques for detection of genomic DNA include Southern hybridizations.In vivo techniques for detection of mRNA include polymerase chainreaction (PCR), Northern hybridizations and in situ hybridizations.Furthermore, in vivo techniques for detection of a marker proteininclude introducing into a subject a labeled antibody directed againstthe protein or fragment thereof. For example, the antibody can belabeled with a radioactive marker whose presence and location in asubject can be detected by standard imaging techniques.

A general principle of such diagnostic and prognostic assays involvespreparing a sample or reaction mixture that may contain a marker, and aprobe, under appropriate conditions and for a time sufficient to allowthe marker and probe to interact and bind, thus forming a complex thatcan be removed and/or detected in the reaction mixture. These assays canbe conducted in a variety of ways.

For example, one method to conduct such an assay would involve anchoringthe marker or probe onto a solid phase support, also referred to as asubstrate, and detecting target marker/probe complexes anchored on thesolid phase at the end of the reaction. In one embodiment of such amethod, a sample from a subject, which is to be assayed for presenceand/or concentration of marker, can be anchored onto a carrier or solidphase support. In another embodiment, the reverse situation is possible,in which the probe can be anchored to a solid phase and a sample from asubject can be allowed to react as an unanchored component of the assay.

There are many established methods for anchoring assay components to asolid phase. These include, without limitation, marker or probemolecules which are immobilized through conjugation of biotin andstreptavidin. Such biotinylated assay components can be prepared frombiotin-NHS (N-hydroxy-succinimide) using techniques known in the art(e.g., biotinylation kit, Pierce Chemicals, Rockford, Ill.), andimmobilized in the wells of streptavidin-coated 96 well plates (PierceChemical). In certain embodiments, the surfaces with immobilized assaycomponents can be prepared in advance and stored.

Other suitable carriers or solid phase supports for such assays includeany material capable of binding the class of molecule to which themarker or probe belongs. Well-known supports or carriers include, butare not limited to, glass, polystyrene, nylon, polypropylene, nylon,polyethylene, dextran, amylases, natural and modified celluloses,polyacrylamides, gabbros, and magnetite.

In order to conduct assays with the above mentioned approaches, thenon-immobilized component is added to the solid phase upon which thesecond component is anchored. After the reaction is complete,uncomplexed components may be removed (e.g., by washing) underconditions such that any complexes formed will remain immobilized uponthe solid phase. The detection of marker/probe complexes anchored to thesolid phase can be accomplished in a number of methods outlined herein.

In a preferred embodiment, the probe, when it is the unanchored assaycomponent, can be labeled for the purpose of detection and readout ofthe assay, either directly or indirectly, with detectable labelsdiscussed herein and which are well-known to one skilled in the art.

It is also possible to directly detect marker/probe complex formationwithout further manipulation or labeling of either component (marker orprobe), for example by utilizing the technique of fluorescence energytransfer (see, for example, Lakowicz et al., U.S. Pat. No. 5,631,169;Stavrianopoulos, et al., U.S. Pat. No. 4,868,103). A fluorophore labelon the first, ‘donor’ molecule is selected such that, upon excitationwith incident light of appropriate wavelength, its emitted fluorescentenergy will be absorbed by a fluorescent label on a second ‘acceptor’molecule, which in turn is able to fluoresce due to the absorbed energy.Alternately, the ‘donor’ protein molecule may simply utilize the naturalfluorescent energy of tryptophan residues. Labels are chosen that emitdifferent wavelengths of light, such that the ‘acceptor’ molecule labelmay be differentiated from that of the ‘donor’. Since the efficiency ofenergy transfer between the labels is related to the distance separatingthe molecules, spatial relationships between the molecules can beassessed. In a situation in which binding occurs between the molecules,the fluorescent emission of the ‘acceptor’ molecule label in the assayshould be maximal. An FET binding event can be conveniently measuredthrough standard fluorometric detection means well known in the art(e.g., using a fluorimeter).

In another embodiment, determination of the ability of a probe torecognize a marker can be accomplished without labeling either assaycomponent (probe or marker) by utilizing a technology such as real-timeBiomolecular Interaction Analysis (BIA) (see, e.g., Sjolander, S, andUrbaniczky, C., 1991, Anal. Chem. 63:2338-2345 and Szabo et al., 1995,Curr. Opin. Struct. Biol. 5:699-705). As used herein, “BIA” or “surfaceplasmon resonance” is a technology for studying biospecific interactionsin real time, without labeling any of the interactants (e.g., BIAcore).Changes in the mass at the binding surface (indicative of a bindingevent) result in alterations of the refractive index of light near thesurface (the optical phenomenon of surface plasmon resonance (SPR)),resulting in a detectable signal which can be used as an indication ofreal-time reactions between biological molecules.

Alternatively, in another embodiment, analogous diagnostic andprognostic assays can be conducted with marker and probe as solutes in aliquid phase. In such an assay, the complexed marker and probe areseparated from uncomplexed components by any of a number of standardtechniques, including but not limited to: differential centrifugation,chromatography, electrophoresis and immunoprecipitation. In differentialcentrifugation, marker/probe complexes may be separated from uncomplexedassay components through a series of centrifugal steps, due to thedifferent sedimentation equilibria of complexes based on their differentsizes and densities (see, for example, Rivas, G., and Minton, A. P.,1993, Trends Biochem Sci. 18(8):284-7). Standard chromatographictechniques may also be utilized to separate complexed molecules fromuncomplexed ones. For example, gel filtration chromatography separatesmolecules based on size, and through the utilization of an appropriategel filtration resin in a column format, for example, the relativelylarger complex may be separated from the relatively smaller uncomplexedcomponents. Similarly, the relatively different charge properties of themarker/probe complex as compared to the uncomplexed components may beexploited to differentiate the complex from uncomplexed components, forexample through the utilization of ion-exchange chromatography resins.Such resins and chromatographic techniques are well known to one skilledin the art (see, e.g., Heegaard, N. H., 1998, J. Mol. Recognit. Winter11(1-6):141-8; Hage, D. S., and Tweed, S. A. J Chromatogr B Biomed SciAppl 1997 Oct. 10; 699(1-2):499-525). Gel electrophoresis may also beemployed to separate complexed assay components from unbound components(see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology,John Wiley & Sons, New York, 1987-1999). In this technique, protein ornucleic acid complexes are separated based on size or charge, forexample. In order to maintain the binding interaction during theelectrophoretic process, non-denaturing gel matrix materials andconditions in the absence of reducing agent are typically preferred.Appropriate conditions to the particular assay and components thereofwill be well known to one skilled in the art.

In a particular embodiment, the level of marker mRNA can be determinedboth by in situ and by in vitro formats in a biological sample usingmethods known in the art. The term “biological sample” is intended toinclude tissues, cells, biological fluids and isolates thereof, isolatedfrom a subject, as well as tissues, cells and fluids present within asubject. Many expression detection methods use isolated RNA. For invitro methods, any RNA isolation technique that does not select againstthe isolation of mRNA can be utilized for the purification of RNA fromcells (see, e.g., Ausubel et al., ed., Current Protocols in MolecularBiology, John Wiley & Sons, New York 1987-1999). Additionally, largenumbers of tissue samples can readily be processed using techniques wellknown to those of skill in the art, such as, for example, thesingle-step RNA isolation process of Chomczynski (1989, U.S. Pat. No.4,843,155).

The isolated mRNA can be used in hybridization or amplification assaysthat include, but are not limited to, Southern or Northern analyses,polymerase chain reaction analyses and probe arrays. One preferreddiagnostic method for the detection of mRNA levels involves contactingthe isolated mRNA with a nucleic acid molecule (probe) that canhybridize to the mRNA encoded by the gene being detected. The nucleicacid probe can be, for example, a full-length cDNA, or a portionthereof, such as an oligonucleotide of at least 7, 15, 30, 50, 100, 250or 500 nucleotides in length and sufficient to specifically hybridizeunder stringent conditions to a mRNA or genomic DNA encoding a marker ofthe present invention. Other suitable probes for use in the diagnosticassays of the invention are described herein. Hybridization of an mRNAwith the probe indicates that the marker in question is being expressed.

In one format, the mRNA is immobilized on a solid surface and contactedwith a probe, for example by running the isolated mRNA on an agarose geland transferring the mRNA from the gel to a membrane, such asnitrocellulose. In an alternative format, the probe(s) are immobilizedon a solid surface and the mRNA is contacted with the probe(s), forexample, in an Affymetrix gene chip array. A skilled artisan can readilyadapt known mRNA detection methods for use in detecting the level ofmRNA encoded by the markers of the present invention.

An alternative method for determining the level of mRNA marker in asample involves the process of nucleic acid amplification, e.g., byRT-PCR (the experimental embodiment set forth in Mullis, 1987, U.S. Pat.No. 4,683,202), ligase chain reaction (Barany, 1991, Proc. Natl. Acad.Sci. USA, 88:189-193), self sustained sequence replication (Guatelli etal., 1990, Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptionalamplification system (Kwoh et al., 1989, Proc. Natl. Acad. Sci. USA86:1173-1177), Q-Beta Replicase (Lizardi et al., 1988, Bio/Technology6:1197), rolling circle replication (Lizardi et al., U.S. Pat. No.5,854,033) or any other nucleic acid amplification method, followed bythe detection of the amplified molecules using techniques well known tothose of skill in the art. These detection schemes are especially usefulfor the detection of nucleic acid molecules if such molecules arepresent in very low numbers. As used herein, amplification primers aredefined as being a pair of nucleic acid molecules that can anneal to 5′or 3′ regions of a gene (plus and minus strands, respectively, orvice-versa) and contain a short region in between. In general,amplification primers are from about 10 to 30 nucleotides in length andflank a region from about 50 to 200 nucleotides in length. Underappropriate conditions and with appropriate reagents, such primerspermit the amplification of a nucleic acid molecule comprising thenucleotide sequence flanked by the primers.

For in situ methods, mRNA does not need to be isolated from the prior todetection. In such methods, a cell or tissue sample isprepared/processed using known histological methods. The sample is thenimmobilized on a support, typically a glass slide, and then contactedwith a probe that can hybridize to mRNA that encodes the marker.

As an alternative to making determinations based on the absoluteexpression level of the marker, determinations may be based on thenormalized expression level of the marker. Expression levels arenormalized by correcting the absolute expression level of a marker bycomparing its expression to the expression of a gene that is not amarker, e.g., a housekeeping gene that is constitutively expressed.Suitable genes for normalization include housekeeping genes such as theactin gene, or epithelial cell-specific genes. This normalization allowsthe comparison of the expression level in one sample, e.g., a patientsample, to another sample, e.g., a non-disease or non-toxic sample, orbetween samples from different sources.

Alternatively, the expression level can be provided as a relativeexpression level. To determine a relative expression level of a marker,the level of expression of the marker is determined for 10 or moresamples of normal versus disease or toxic cell isolates, preferably 50or more samples, prior to the determination of the expression level forthe sample in question. The mean expression level of each of the genesassayed in the larger number of samples is determined and this is usedas a baseline expression level for the marker. The expression level ofthe marker determined for the test sample (absolute level of expression)is then divided by the mean expression value obtained for that marker.This provides a relative expression level.

Preferably, the samples used in the baseline determination will be fromnon-disease or non-toxic cells. The choice of the cell source isdependent on the use of the relative expression level. Using expressionfound in normal tissues as a mean expression score aids in validatingwhether the marker assayed is disease or toxicity specific (versusnormal cells). In addition, as more data is accumulated, the meanexpression value can be revised, providing improved relative expressionvalues based on accumulated data. Expression data from disease cells ortoxic cells provides a means for grading the severity of the disease ortoxic state.

In another embodiment of the present invention, a marker protein isdetected. A preferred agent for detecting marker protein of theinvention is an antibody capable of binding to such a protein or afragment thereof, preferably an antibody with a detectable label.Antibodies can be polyclonal, or more preferably, monoclonal. An intactantibody, or a fragment or derivative thereof (e.g., Fab or F(ab′)₂) canbe used. The term “labeled”, with regard to the probe or antibody, isintended to encompass direct labeling of the probe or antibody bycoupling (i.e., physically linking) a detectable substance to the probeor antibody, as well as indirect labeling of the probe or antibody byreactivity with another reagent that is directly labeled. Examples ofindirect labeling include detection of a primary antibody using afluorescently labeled secondary antibody and end-labeling of a DNA probewith biotin such that it can be detected with fluorescently labeledstreptavidin.

Proteins from cells can be isolated using techniques that are well knownto those of skill in the art. The protein isolation methods employedcan, for example, be such as those described in Harlow and Lane (Harlowand Lane, 1988, Antibodies: A Laboratory Manual, Cold Spring HarborLaboratory Press, Cold Spring Harbor, N.Y.).

A variety of formats can be employed to determine whether a samplecontains a protein that binds to a given antibody. Examples of suchformats include, but are not limited to, enzyme immunoassay (EIA),radioimmunoassay (RIA), Western blot analysis and enzyme linkedimmunoabsorbant assay (ELISA). A skilled artisan can readily adapt knownprotein/antibody detection methods for use in determining whether cellsexpress a marker of the present invention.

In one format, antibodies, or antibody fragments or derivatives, can beused in methods such as Western blots or immunofluorescence techniquesto detect the expressed proteins. In such uses, it is generallypreferable to immobilize either the antibody or proteins on a solidsupport. Suitable solid phase supports or carriers include any supportcapable of binding an antigen or an antibody. Well-known supports orcarriers include glass, polystyrene, polypropylene, polyethylene,dextran, nylon, amylases, natural and modified celluloses,polyacrylamides, gabbros, and magnetite.

One skilled in the art will know many other suitable carriers forbinding antibody or antigen, and will be able to adapt such support foruse with the present invention. For example, protein isolated fromdisease or toxic cells can be run on a polyacrylamide gelelectrophoresis and immobilized onto a solid phase support such asnitrocellulose. The support can then be washed with suitable buffersfollowed by treatment with the detectably labeled antibody. The solidphase support can then be washed with the buffer a second time to removeunbound antibody. The amount of bound label on the solid support canthen be detected by conventional means.

The invention also encompasses kits for detecting the presence of amarker protein or nucleic acid in a biological sample. Such kits can beused to determine if a subject is suffering from or is at increased riskof developing certain diseases or drug-induced toxicity. For example,the kit can comprise a labeled compound or agent capable of detecting amarker protein or nucleic acid in a biological sample and means fordetermining the amount of the protein or mRNA in the sample (e.g., anantibody which binds the protein or a fragment thereof, or anoligonucleotide probe which binds to DNA or mRNA encoding the protein).Kits can also include instructions for interpreting the results obtainedusing the kit.

For antibody-based kits, the kit can comprise, for example: (1) a firstantibody (e.g., attached to a solid support) which binds to a markerprotein; and, optionally, (2) a second, different antibody which bindsto either the protein or the first antibody and is conjugated to adetectable label.

For oligonucleotide-based kits, the kit can comprise, for example: (1)an oligonucleotide, e.g., a detectably labeled oligonucleotide, whichhybridizes to a nucleic acid sequence encoding a marker protein or (2) apair of primers useful for amplifying a marker nucleic acid molecule.The kit can also comprise, e.g., a buffering agent, a preservative, or aprotein stabilizing agent. The kit can further comprise componentsnecessary for detecting the detectable label (e.g., an enzyme or asubstrate). The kit can also contain a control sample or a series ofcontrol samples which can be assayed and compared to the test sample.Each component of the kit can be enclosed within an individual containerand all of the various containers can be within a single package, alongwith instructions for interpreting the results of the assays performedusing the kit.

G. Pharmacogenomics

The markers of the invention are also useful as pharmacogenomic markers.As used herein, a “pharmacogenomic marker” is an objective biochemicalmarker whose expression level correlates with a specific clinical drugresponse or susceptibility in a patient (see, e.g., McLeod et al. (1999)Eur. J. Cancer 35(12): 1650-1652). The presence or quantity of thepharmacogenomic marker expression is related to the predicted responseof the patient and more particularly the patient's diseased or toxiccells to therapy with a specific drug or class of drugs. By assessingthe presence or quantity of the expression of one or morepharmacogenomic markers in a patient, a drug therapy which is mostappropriate for the patient, or which is predicted to have a greaterdegree of success, may be selected. For example, based on the presenceor quantity of RNA or protein encoded by specific tumor markers in apatient, a drug or course of treatment may be selected that is optimizedfor the treatment of the specific tumor likely to be present in thepatient. The use of pharmacogenomic markers therefore permits selectingor designing the most appropriate treatment for each cancer patientwithout trying different drugs or regimes.

Another aspect of pharmacogenomics deals with genetic conditions thatalters the way the body acts on drugs. These pharmacogenetic conditionscan occur either as rare defects or as polymorphisms. For example,glucose-6-phosphate dehydrogenase (G6PD) deficiency is a commoninherited enzymopathy in which the main clinical complication ishemolysis after ingestion of oxidant drugs (anti-malarials,sulfonamides, analgesics, nitrofurans) and consumption of fava beans.

As an illustrative embodiment, the activity of drug metabolizing enzymesis a major determinant of both the intensity and duration of drugaction. The discovery of genetic polymorphisms of drug metabolizingenzymes (e.g., N-acetyltransferase 2 (NAT 2) and cytochrome P450 enzymesCYP2D6 and CYP2C19) has provided an explanation as to why some patientsdo not obtain the expected drug effects or show exaggerated drugresponse and serious toxicity after taking the standard and safe dose ofa drug. These polymorphisms are expressed in two phenotypes in thepopulation, the extensive metabolizer (EM) and poor metabolizer (PM).The prevalence of PM is different among different populations. Forexample, the gene coding for CYP2D6 is highly polymorphic and severalmutations have been identified in PM, which all lead to the absence offunctional CYP2D6. Poor metabolizers of CYP2D6 and CYP2C19 quitefrequently experience exaggerated drug response and side effects whenthey receive standard doses. If a metabolite is the active therapeuticmoiety, a PM will show no therapeutic response, as demonstrated for theanalgesic effect of codeine mediated by its CYP2D6-formed metabolitemorphine. The other extreme are the so called ultra-rapid metabolizerswho do not respond to standard doses. Recently, the molecular basis ofultra-rapid metabolism has been identified to be due to CYP2D6 geneamplification.

Thus, the level of expression of a marker of the invention in anindividual can be determined to thereby select appropriate agent(s) fortherapeutic or prophylactic treatment of the individual. In addition,pharmacogenetic studies can be used to apply genotyping of polymorphicalleles encoding drug-metabolizing enzymes to the identification of anindividual's drug responsiveness phenotype. This knowledge, when appliedto dosing or drug selection, can avoid adverse reactions or therapeuticfailure and thus enhance therapeutic or prophylactic efficiency whentreating a subject with a modulator of expression of a marker of theinvention.

H. Monitoring Clinical Trials

Monitoring the influence of agents (e.g., drug compounds) on the levelof expression of a marker of the invention can be applied not only inbasic drug screening, but also in clinical trials. For example, theeffectiveness of an agent to affect marker expression can be monitoredin clinical trials of subjects receiving treatment for certain diseases,such as cancer, diabetes, obesity, cardiovescular disease, andcardiotoxicity, or drug-induced toxicity. In a preferred embodiment, thepresent invention provides a method for monitoring the effectiveness oftreatment of a subject with an agent (e.g., an agonist, antagonist,peptidomimetic, protein, peptide, nucleic acid, small molecule, or otherdrug candidate) comprising the steps of (i) obtaining apre-administration sample from a subject prior to administration of theagent; (ii) detecting the level of expression of one or more selectedmarkers of the invention in the pre-administration sample; (iii)obtaining one or more post-administration samples from the subject; (iv)detecting the level of expression of the marker(s) in thepost-administration samples; (v) comparing the level of expression ofthe marker(s) in the pre-administration sample with the level ofexpression of the marker(s) in the post-administration sample orsamples; and (vi) altering the administration of the agent to thesubject accordingly. For example, increased expression of the markergene(s) during the course of treatment may indicate ineffective dosageand the desirability of increasing the dosage. Conversely, decreasedexpression of the marker gene(s) may indicate efficacious treatment andno need to change dosage.

H. Arrays

The invention also includes an array comprising a marker of the presentinvention. The array can be used to assay expression of one or moregenes in the array. In one embodiment, the array can be used to assaygene expression in a tissue to ascertain tissue specificity of genes inthe array. In this manner, up to about 7600 genes can be simultaneouslyassayed for expression. This allows a profile to be developed showing abattery of genes specifically expressed in one or more tissues.

In addition to such qualitative determination, the invention allows thequantitation of gene expression. Thus, not only tissue specificity, butalso the level of expression of a battery of genes in the tissue isascertainable. Thus, genes can be grouped on the basis of their tissueexpression per se and level of expression in that tissue. This isuseful, for example, in ascertaining the relationship of gene expressionbetween or among tissues. Thus, one tissue can be perturbed and theeffect on gene expression in a second tissue can be determined. In thiscontext, the effect of one cell type on another cell type in response toa biological stimulus can be determined. Such a determination is useful,for example, to know the effect of cell-cell interaction at the level ofgene expression. If an agent is administered therapeutically to treatone cell type but has an undesirable effect on another cell type, theinvention provides an assay to determine the molecular basis of theundesirable effect and thus provides the opportunity to co-administer acounteracting agent or otherwise treat the undesired effect. Similarly,even within a single cell type, undesirable biological effects can bedetermined at the molecular level. Thus, the effects of an agent onexpression of other than the target gene can be ascertained andcounteracted.

In another embodiment, the array can be used to monitor the time courseof expression of one or more genes in the array. This can occur invarious biological contexts, as disclosed herein, for exampledevelopment of drug-induced toxicity, progression of drug-inducedtoxicity, and processes, such a cellular transformation associated withdrug-induced toxicity.

The array is also useful for ascertaining the effect of the expressionof a gene on the expression of other genes in the same cell or indifferent cells. This provides, for example, for a selection ofalternate molecular targets for therapeutic intervention if the ultimateor downstream target cannot be regulated.

The array is also useful for ascertaining differential expressionpatterns of one or more genes in normal and abnormal cells. Thisprovides a battery of genes that could serve as a molecular target fordiagnosis or therapeutic intervention.

VII. Methods for Obtaining Samples

Samples useful in the methods of the invention include any tissue, cell,biopsy, or bodily fluid sample that expresses a marker of the invention.In one embodiment, a sample may be a tissue, a cell, whole blood, serum,plasma, buccal scrape, saliva, cerebrospinal fluid, urine, stool, orbronchoalveolar lavage. In preferred embodiments, the tissue sample is adisease state or toxicity state sample. In more preferred embodiments,the tissue sample is a cancer sample, a diabetes sample, an obesitysample, a cardiovascular sample or a drug-induced toxicity sample.

Body samples may be obtained from a subject by a variety of techniquesknown in the art including, for example, by the use of a biopsy or byscraping or swabbing an area or by using a needle to aspirate bodilyfluids. Methods for collecting various body samples are well known inthe art.

Tissue samples suitable for detecting and quantitating a marker of theinvention may be fresh, frozen, or fixed according to methods known toone of skill in the art. Suitable tissue samples are preferablysectioned and placed on a microscope slide for further analyses.Alternatively, solid samples, i.e., tissue samples, may be solubilizedand/or homogenized and subsequently analyzed as soluble extracts.

In one embodiment, a freshly obtained biopsy sample is frozen using, forexample, liquid nitrogen or difluorodichloromethane. The frozen sampleis mounted for sectioning using, for example, OCT, and seriallysectioned in a cryostat. The serial sections are collected on a glassmicroscope slide. For immunohistochemical staining the slides may becoated with, for example, chrome-alum, gelatine or poly-L-lysine toensure that the sections stick to the slides. In another embodiment,samples are fixed and embedded prior to sectioning. For example, atissue sample may be fixed in, for example, formalin, seriallydehydrated and embedded in, for example, paraffin.

Once the sample is obtained any method known in the art to be suitablefor detecting and quantitating a marker of the invention may be used(either at the nucleic acid or at the protein level). Such methods arewell known in the art and include but are not limited to western blots,northern blots, southern blots, immunohistochemistry, ELISA, e.g.,amplified ELISA, immunoprecipitation, immunofluorescence, flowcytometry, immunocytochemistry, mass spectrometrometric analyses, e.g.,MALDI-TOF and SELDI-TOF, nucleic acid hybridization techniques, nucleicacid reverse transcription methods, and nucleic acid amplificationmethods. In particular embodiments, the expression of a marker of theinvention is detected on a protein level using, for example, antibodiesthat specifically bind these proteins.

Samples may need to be modified in order to make a marker of theinvention accessible to antibody binding. In a particular aspect of theimmunocytochemistry or immunohistochemistry methods, slides may betransferred to a pretreatment buffer and optionally heated to increaseantigen accessibility. Heating of the sample in the pretreatment bufferrapidly disrupts the lipid bi-layer of the cells and makes the antigens(may be the case in fresh specimens, but not typically what occurs infixed specimens) more accessible for antibody binding. The terms“pretreatment buffer” and “preparation buffer” are used interchangeablyherein to refer to a buffer that is used to prepare cytology orhistology samples for immunostaining, particularly by increasing theaccessibility of a marker of the invention for antibody binding. Thepretreatment buffer may comprise a pH-specific salt solution, a polymer,a detergent, or a nonionic or anionic surfactant such as, for example,an ethyloxylated anionic or nonionic surfactant, an alkanoate or analkoxylate or even blends of these surfactants or even the use of a bilesalt. The pretreatment buffer may, for example, be a solution of 0.1% to1% of deoxycholic acid, sodium salt, or a solution of sodiumlaureth-13-carboxylate (e.g., Sandopan LS) or and ethoxylated anioniccomplex. In some embodiments, the pretreatment buffer may also be usedas a slide storage buffer.

Any method for making marker proteins of the invention more accessiblefor antibody binding may be used in the practice of the invention,including the antigen retrieval methods known in the art. See, forexample, Bibbo, et al. (2002) Acta. Cytol. 46:25-29; Saqi, et al. (2003)Diagn. Cytopathol. 27:365-370; Bibbo, et al. (2003) Anal. Quant. Cytol.Histol. 25:8-11, the entire contents of each of which are incorporatedherein by reference.

Following pretreatment to increase marker protein accessibility, samplesmay be blocked using an appropriate blocking agent, e.g., a peroxidaseblocking reagent such as hydrogen peroxide. In some embodiments, thesamples may be blocked using a protein blocking reagent to preventnon-specific binding of the antibody. The protein blocking reagent maycomprise, for example, purified casein. An antibody, particularly amonoclonal or polyclonal antibody that specifically binds to a marker ofthe invention is then incubated with the sample. One of skill in the artwill appreciate that a more accurate prognosis or diagnosis may beobtained in some cases by detecting multiple epitopes on a markerprotein of the invention in a patient sample. Therefore, in particularembodiments, at least two antibodies directed to different epitopes of amarker of the invention are used. Where more than one antibody is used,these antibodies may be added to a single sample sequentially asindividual antibody reagents or simultaneously as an antibody cocktail.Alternatively, each individual antibody may be added to a separatesample from the same patient, and the resulting data pooled.

Techniques for detecting antibody binding are well known in the art.Antibody binding to a marker of the invention may be detected throughthe use of chemical reagents that generate a detectable signal thatcorresponds to the level of antibody binding and, accordingly, to thelevel of marker protein expression. In one of the immunohistochemistryor immunocytochemistry methods of the invention, antibody binding isdetected through the use of a secondary antibody that is conjugated to alabeled polymer. Examples of labeled polymers include but are notlimited to polymer-enzyme conjugates. The enzymes in these complexes aretypically used to catalyze the deposition of a chromogen at theantigen-antibody binding site, thereby resulting in cell staining thatcorresponds to expression level of the biomarker of interest. Enzymes ofparticular interest include, but are not limited to, horseradishperoxidase (HRP) and alkaline phosphatase (AP).

In one particular immunohistochemistry or immunocytochemistry method ofthe invention, antibody binding to a marker of the invention is detectedthrough the use of an HRP-labeled polymer that is conjugated to asecondary antibody. Antibody binding can also be detected through theuse of a species-specific probe reagent, which binds to monoclonal orpolyclonal antibodies, and a polymer conjugated to HRP, which binds tothe species specific probe reagent. Slides are stained for antibodybinding using any chromagen, e.g., the chromagen 3,3-diaminobenzidine(DAB), and then counterstained with hematoxylin and, optionally, abluing agent such as ammonium hydroxide or TBS/Tween-20. Other suitablechromagens include, for example, 3-amino-9-ethylcarbazole (AEC). In someaspects of the invention, slides are reviewed microscopically by acytotechnologist and/or a pathologist to assess cell staining, e.g.,fluorescent staining (i.e., marker expression). Alternatively, samplesmay be reviewed via automated microscopy or by personnel with theassistance of computer software that facilitates the identification ofpositive staining cells.

Detection of antibody binding can be facilitated by coupling theanti-marker antibodies to a detectable substance. Examples of detectablesubstances include various enzymes, prosthetic groups, fluorescentmaterials, luminescent materials, bioluminescent materials, andradioactive materials. Examples of suitable enzymes include horseradishperoxidase, alkaline phosphatase, β-galactosidase, oracetylcholinesterase; examples of suitable prosthetic group complexesinclude streptavidin/biotin and avidin/biotin; examples of suitablefluorescent materials include umbelliferone, fluorescein, fluoresceinisothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansylchloride or phycoerythrin; an example of a luminescent material includesluminol; examples of bioluminescent materials include luciferase,luciferin, and aequorin; and examples of suitable radioactive materialinclude ¹²⁵I, ¹³¹I, ³⁵S, ¹⁴C, or ³H.

In one embodiment of the invention frozen samples are prepared asdescribed above and subsequently stained with antibodies against amarker of the invention diluted to an appropriate concentration using,for example, Tris-buffered saline (TBS). Primary antibodies can bedetected by incubating the slides in biotinylated anti-immunoglobulin.This signal can optionally be amplified and visualized usingdiaminobenzidine precipitation of the antigen. Furthermore, slides canbe optionally counterstained with, for example, hematoxylin, tovisualize the cells.

In another embodiment, fixed and embedded samples are stained withantibodies against a marker of the invention and counterstained asdescribed above for frozen sections. In addition, samples may beoptionally treated with agents to amplify the signal in order tovisualize antibody staining. For example, a peroxidase-catalyzeddeposition of biotinyl-tyramide, which in turn is reacted withperoxidase-conjugated streptavidin (Catalyzed Signal Amplification (CSA)System, DAKO, Carpinteria, Calif.) may be used.

Tissue-based assays (i.e., immunohistochemistry) are the preferredmethods of detecting and quantitating a marker of the invention. In oneembodiment, the presence or absence of a marker of the invention may bedetermined by immunohistochemistry. In one embodiment, theimmunohistochemical analysis uses low concentrations of an anti-markerantibody such that cells lacking the marker do not stain. In anotherembodiment, the presence or absence of a marker of the invention isdetermined using an immunohistochemical method that uses highconcentrations of an anti-marker antibody such that cells lacking themarker protein stain heavily. Cells that do not stain contain eithermutated marker and fail to produce antigenically recognizable markerprotein, or are cells in which the pathways that regulate marker levelsare dysregulated, resulting in steady state expression of negligiblemarker protein.

One of skill in the art will recognize that the concentration of aparticular antibody used to practice the methods of the invention willvary depending on such factors as time for binding, level of specificityof the antibody for a marker of the invention, and method of samplepreparation. Moreover, when multiple antibodies are used, the requiredconcentration may be affected by the order in which the antibodies areapplied to the sample, e.g., simultaneously as a cocktail orsequentially as individual antibody reagents. Furthermore, the detectionchemistry used to visualize antibody binding to a marker of theinvention must also be optimized to produce the desired signal to noiseratio.

In one embodiment of the invention, proteomic methods, e.g., massspectrometry, are used for detecting and quantitating the markerproteins of the invention. For example, matrix-associated laserdesorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) orsurface-enhanced laser desorption/ionization time-of-flight massspectrometry (SELDI-TOF MS) which involves the application of abiological sample, such as serum, to a protein-binding chip (Wright, G.L., Jr., et al. (2002) Expert Rev Mol Diagn 2:549; Li, J., et al. (2002)Clin Chem 48:1296; Laronga, C., et al. (2003) Dis Markers 19:229;Petricoin, E. F., et al. (2002) 359:572; Adam, B. L., et al. (2002)Cancer Res 62:3609; Tolson, J., et al. (2004) Lab Invest 84:845; Xiao,Z., et al. (2001) Cancer Res 61:6029) can be used to detect andquantitate the PY-Shc and/or p66-Shc proteins. Mass spectrometricmethods are described in, for example, U.S. Pat. Nos. 5,622,824,5,605,798 and 5,547,835, the entire contents of each of which areincorporated herein by reference.

In other embodiments, the expression of a marker of the invention isdetected at the nucleic acid level. Nucleic acid-based techniques forassessing expression are well known in the art and include, for example,determining the level of marker mRNA in a sample from a subject. Manyexpression detection methods use isolated RNA. Any RNA isolationtechnique that does not select against the isolation of mRNA can beutilized for the purification of RNA from cells that express a marker ofthe invention (see, e.g., Ausubel et al., ed., (1987-1999) CurrentProtocols in Molecular Biology (John Wiley & Sons, New York).Additionally, large numbers of tissue samples can readily be processedusing techniques well known to those of skill in the art, such as, forexample, the single-step RNA isolation process of Chomczynski (1989,U.S. Pat. No. 4,843,155).

The term “probe” refers to any molecule that is capable of selectivelybinding to a marker of the invention, for example, a nucleotidetranscript and/or protein. Probes can be synthesized by one of skill inthe art, or derived from appropriate biological preparations. Probes maybe specifically designed to be labeled. Examples of molecules that canbe utilized as probes include, but are not limited to, RNA, DNA,proteins, antibodies, and organic molecules.

Isolated mRNA can be used in hybridization or amplification assays thatinclude, but are not limited to, Southern or Northern analyses,polymerase chain reaction analyses and probe arrays. One method for thedetection of mRNA levels involves contacting the isolated mRNA with anucleic acid molecule (probe) that can hybridize to the marker mRNA. Thenucleic acid probe can be, for example, a full-length cDNA, or a portionthereof, such as an oligonucleotide of at least 7, 15, 30, 50, 100, 250or 500 nucleotides in length and sufficient to specifically hybridizeunder stringent conditions to marker genomic DNA.

In one embodiment, the mRNA is immobilized on a solid surface andcontacted with a probe, for example by running the isolated mRNA on anagarose gel and transferring the mRNA from the gel to a membrane, suchas nitrocellulose. In an alternative embodiment, the probe(s) areimmobilized on a solid surface and the mRNA is contacted with theprobe(s), for example, in an Affymetrix gene chip array. A skilledartisan can readily adapt known mRNA detection methods for use indetecting the level of marker mRNA.

An alternative method for determining the level of marker mRNA in asample involves the process of nucleic acid amplification, e.g., byRT-PCR (the experimental embodiment set forth in Mullis, 1987, U.S. Pat.No. 4,683,202), ligase chain reaction (Barany (1991) Proc. Natl. Acad.Sci. USA 88:189-193), self sustained sequence replication (Guatelli etal. (1990) Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptionalamplification system (Kwoh et al. (1989) Proc. Natl. Acad. Sci. USA86:1173-1177), Q-Beta Replicase (Lizardi et al. (1988) Bio/Technology6:1197), rolling circle replication (Lizardi et al., U.S. Pat. No.5,854,033) or any other nucleic acid amplification method, followed bythe detection of the amplified molecules using techniques well known tothose of skill in the art. These detection schemes are especially usefulfor the detection of nucleic acid molecules if such molecules arepresent in very low numbers. In particular aspects of the invention,marker expression is assessed by quantitative fluorogenic RT-PCR (i.e.,the TaqMan™ System). Such methods typically utilize pairs ofoligonucleotide primers that are specific for a marker of the invention.Methods for designing oligonucleotide primers specific for a knownsequence are well known in the art.

The expression levels of a marker of the invention may be monitoredusing a membrane blot (such as used in hybridization analysis such asNorthern, Southern, dot, and the like), or microwells, sample tubes,gels, beads or fibers (or any solid support comprising bound nucleicacids). See U.S. Pat. Nos. 5,770,722, 5,874,219, 5,744,305, 5,677,195and 5,445,934, which are incorporated herein by reference. The detectionof marker expression may also comprise using nucleic acid probes insolution.

In one embodiment of the invention, microarrays are used to detect theexpression of a marker of the invention. Microarrays are particularlywell suited for this purpose because of the reproducibility betweendifferent experiments. DNA microarrays provide one method for thesimultaneous measurement of the expression levels of large numbers ofgenes. Each array consists of a reproducible pattern of capture probesattached to a solid support. Labeled RNA or DNA is hybridized tocomplementary probes on the array and then detected by laser scanning.Hybridization intensities for each probe on the array are determined andconverted to a quantitative value representing relative gene expressionlevels. See, U.S. Pat. Nos. 6,040,138, 5,800,992 and 6,020,135,6,033,860, and 6,344,316, which are incorporated herein by reference.High-density oligonucleotide arrays are particularly useful fordetermining the gene expression profile for a large number of RNA's in asample.

The amounts of marker, and/or a mathematical relationship of the amountsof a marker of the invention may be used to calculate the risk ofrecurrence of a disease state, e.g. cancer, diabetes, obesity,cardiovascular disease, or a toxicity state, e.g., a drug-inducedtoxicity or cardiotoxicity, in a subject being treated for a diseasestate or toxicity state, the survival of a subject being treated for adisease state or a toxicity state, whether a disease state or toxicitystate is aggressive, the efficacy of a treatment regimen for treating adisease state or toxicity state, and the like, using the methods of theinvention, which may include methods of regression analysis known to oneof skill in the art. For example, suitable regression models include,but are not limited to CART (e.g., Hill, T, and Lewicki, P. (2006)“STATISTICS Methods and Applications” StatSoft, Tulsa, Okla.), Cox(e.g., www.evidence-based-medicine.co.uk), exponential, normal and lognormal (e.g., www.obgyn.cam.ac.uk/mrg/statsbook/stsurvan.html), logistic(e.g., www.en.wikipedia.org/wiki/Logistic_regression), parametric,non-parametric, semi-parametric (e.g.,www.socserv.mcmaster.ca/jfox/Books/Companion), linear (e.g.,www.en.wikipedia.org/wiki/Linear_regression), or additive (e.g.,www.en.wikipedia.org/wiki/Generalized_additive_model).

In one embodiment, a regression analysis includes the amounts of marker.In another embodiment, a regression analysis includes a markermathematical relationship. In yet another embodiment, a regressionanalysis of the amounts of marker, and/or a marker mathematicalrelationship may include additional clinical and/or molecularco-variates. Such clinical co-variates include, but are not limited to,nodal status, tumor stage, tumor grade, tumor size, treatment regime,e.g., chemotherapy and/or radiation therapy, clinical outcome (e.g.,relapse, disease-specific survival, therapy failure), and/or clinicaloutcome as a function of time after diagnosis, time after initiation oftherapy, and/or time after completion of treatment.

VIII. Kits

The invention also provides compositions and kits for prognosing adisease state, e.g. cancer, diabetes, obesity, cardiovascular disease,or a toxicity state, e.g., a drug-induced toxicity or cardiotoxicity,recurrence of a disease state or a toxicity state, or survival of asubject being treated for a disease state or a toxicity state. Thesekits include one or more of the following: a detectable antibody thatspecifically binds to a marker of the invention, a detectable antibodythat specifically binds to a marker of the invention, reagents forobtaining and/or preparing subject tissue samples for staining, andinstructions for use.

The kits of the invention may optionally comprise additional componentsuseful for performing the methods of the invention. By way of example,the kits may comprise fluids (e.g., SSC buffer) suitable for annealingcomplementary nucleic acids or for binding an antibody with a proteinwith which it specifically binds, one or more sample compartments, aninstructional material which describes performance of a method of theinvention and tissue specific controls/standards.

IX. Screening Assays

Targets of the invention include, but are not limited to, the genesand/or proteins listed herein. Based on the results of experimentsdescribed by Applicants herein, the key proteins modulated in a diseasestate or a toxicity state are associated with or can be classified intodifferent pathways or groups of molecules, including cytoskeletalcomponents, transcription factors, apoptotic response, pentose phosphatepathway, biosynthetic pathway, oxidative stress (pro-oxidant), membranealterations, and oxidative phosphorylation metabolism. Accordingly, inone embodiment of the invention, a marker may include one or more genes(or proteins) selected from the group consisting of HSPA8, FLNB, PARK7,HSPA1A/HSPA1B, ST13, TUBB3, MIF, KARS, NARS, LGALS1, DDX17, EIF5A,HSPA5, DHX9, HNRNPC, CKAP4, HSPA9, PARP1, HADHA, PHB2, ATP5A1, CANX,GRP78, GRP75, TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1, GPAT1 and TAZ. Inone embodiment, a marker may include one or more genes (or proteins)selected from the group consisting of GRP78, GRP75, TIMP1, PTX3, HSP76,PDIA4, PDIA1, CA2D1, GPAT1 and TAZ. In some embodiments, the markers area combination of at least two, three, four, five, six, seven, eight,nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen,seventeen, eighteen, nineteen, twenty, twenty-five, thirty, or more ofthe foregoing genes (or proteins).

Screening assays useful for identifying modulators of identified markersare described below.

The invention also provides methods (also referred to herein as“screening assays”) for identifying modulators, i.e., candidate or testcompounds or agents (e.g., proteins, peptides, peptidomimetics,peptoids, small molecules or other drugs), which are useful for treatingor preventing a disease state or a toxicity state by modulating theexpression and/or activity of a marker of the invention. Such assaystypically comprise a reaction between a marker of the invention and oneor more assay components. The other components may be either the testcompound itself, or a combination of test compounds and a naturalbinding partner of a marker of the invention. Compounds identified viaassays such as those described herein may be useful, for example, formodulating, e.g., inhibiting, ameliorating, treating, or preventingaggressiveness of a disease state or toxicity state.

The test compounds used in the screening assays of the present inventionmay be obtained from any available source, including systematiclibraries of natural and/or synthetic compounds. Test compounds may alsobe obtained by any of the numerous approaches in combinatorial librarymethods known in the art, including: biological libraries; peptoidlibraries (libraries of molecules having the functionalities ofpeptides, but with a novel, non-peptide backbone which are resistant toenzymatic degradation but which nevertheless remain bioactive; see,e.g., Zuckermann et al., 1994, J. Med. Chem. 37:2678-85); spatiallyaddressable parallel solid phase or solution phase libraries; syntheticlibrary methods requiring deconvolution; the ‘one-bead one-compound’library method; and synthetic library methods using affinitychromatography selection. The biological library and peptoid libraryapproaches are limited to peptide libraries, while the other fourapproaches are applicable to peptide, non-peptide oligomer or smallmolecule libraries of compounds (Lam, 1997, Anticancer Drug Des.12:145).

Examples of methods for the synthesis of molecular libraries can befound in the art, for example in: DeWitt et al. (1993) Proc. Natl. Acad.Sci. U.S.A. 90:6909; Erb et al. (1994) Proc. Natl. Acad. Sci. USA91:11422; Zuckermann et al. (1994). J. Med. Chem. 37:2678; Cho et al.(1993) Science 261:1303; Carrell et al. (1994) Angew. Chem. Int. Ed.Engl. 33:2059; Carell et al. (1994) Angew. Chem. Int. Ed. Engl. 33:2061;and in Gallop et al. (1994) J. Med. Chem. 37:1233.

Libraries of compounds may be presented in solution (e.g., Houghten,1992, Biotechniques 13:412-421), or on beads (Lam, 1991, Nature354:82-84), chips (Fodor, 1993, Nature 364:555-556), bacteria and/orspores, (Ladner, U.S. Pat. No. 5,223,409), plasmids (Cull et al, 1992,Proc Natl Acad Sci USA 89:1865-1869) or on phage (Scott and Smith, 1990,Science 249:386-390; Devlin, 1990, Science 249:404-406; Cwirla et al,1990, Proc. Natl. Acad. Sci. 87:6378-6382; Felici, 1991, J. Mol. Biol.222:301-310; Ladner, supra.).

The screening methods of the invention comprise contacting a diseasestate cell or a toxicity state cell with a test compound and determiningthe ability of the test compound to modulate the expression and/oractivity of a marker of the invention in the cell. The expression and/oractivity of a marker of the invention can be determined as describedherein.

In another embodiment, the invention provides assays for screeningcandidate or test compounds which are substrates of a marker of theinvention or biologically active portions thereof. In yet anotherembodiment, the invention provides assays for screening candidate ortest compounds which bind to a marker of the invention or biologicallyactive portions thereof. Determining the ability of the test compound todirectly bind to a marker can be accomplished, for example, by couplingthe compound with a radioisotope or enzymatic label such that binding ofthe compound to the marker can be determined by detecting the labeledmarker compound in a complex. For example, compounds (e.g., markersubstrates) can be labeled with ¹³¹I, ¹²⁵I, ³⁵S, ¹⁴C, or ³H, eitherdirectly or indirectly, and the radioisotope detected by direct countingof radioemission or by scintillation counting. Alternatively, assaycomponents can be enzymatically labeled with, for example, horseradishperoxidase, alkaline phosphatase, or luciferase, and the enzymatic labeldetected by determination of conversion of an appropriate substrate toproduct.

This invention further pertains to novel agents identified by theabove-described screening assays. Accordingly, it is within the scope ofthis invention to further use an agent identified as described herein inan appropriate animal model. For example, an agent capable of modulatingthe expression and/or activity of a marker of the invention identifiedas described herein can be used in an animal model to determine theefficacy, toxicity, or side effects of treatment with such an agent.Alternatively, an agent identified as described herein can be used in ananimal model to determine the mechanism of action of such an agent.Furthermore, this invention pertains to uses of novel agents identifiedby the above-described screening assays for treatment as describedabove.

EXEMPLIFICATION OF THE INVENTION Example 1: Employing PlatformTechnology to Build a Cancer Consensus and Simulation Networks

In this example, the platform technology described in detail above wasemployed to integrate data obtained from a custom built in vitro cancermodel, and thereby identify novel proteins/pathways driving thepathogenesis of cancer. Relational maps resulting from this analysishave provided cancer treatment targets, as well as diagnostic/prognosticmarkers associated with cancer.

The study design is depicted in FIG. 18. Briefly, two cancer cell lines(PaCa2, HepG2) and one normal cell line (THLE2) were subjected to one ofseven conditions simulating an environment experienced by cancer cellsin vivo. Specifically, cells were exposed to hyperglycemic condition,hypoxia condition, lactic acid condition, hyperglycemic+hypoxiacombination condition, hyperglycemic+lactic acid combination condition,hypoxia+lactic acid combination condition, orhyperglycemic+hypoxia+lactic acid combination condition. Differentconditions were created as the following:

-   -   Hyperglycemic condition was created by culturing the cells in        media containing 22 mM glucose.    -   Hypoxia condition was induced by placing the cells in a Modular        Incubator Chamber (MIC-101, Billups-Rothenberg Inc. Del Mar,        Calif.), which was flooded with an industrial gas mix containing        5% CO₂, 2% O₂ and 93% nitrogen.    -   Lactic acid condition was created by culturing the cells in        media containing 12.5 mM lactic acid.    -   Hyperglycemic+hypoxia combination condition was created by        culturing the cells in media containing 22 mM glucose and the        cells were placed in a Modular Incubator Chamber flooded with an        industrial gas mix containing 5% CO₂, 2% O₂ and 93% nitrogen.    -   Hyperglycemic+lactic acid combination condition was created by        culturing the cells in media containing 22 mM glucose and 12.5        mM lactic acid.    -   Hypoxia+lactic acid combination condition was created by        culturing the cells in media containing 12.5 mM lactic acid and        the cells were placed in a Modular Incubator Chamber flooded        with an industrial gas mix containing 5% CO₂, 2% O₂ and 93%        nitrogen.    -   Hyperglycemic+hypoxia+lactic acid combination condition was        created by culturing the cells in media containing 22 mM glucose        and 12.5 mM lactic acid, and the cells were placed in a Modular        Incubator Chamber flooded with an industrial gas mix containing        5% CO₂, 2% O₂ and 93% nitrogen.

The cell model comprising the above-mentioned cells, wherein the cellswere exposed to each condition described above, was additionallyinterrogated by exposing the cells to an environmental perturbation bytreating with Coenzyme Q10. Specifically, the cells were treated withCoenzyme Q10 at 0, 50 μM, or 100 μM.

Cell samples as well as media samples for each cell line with eachcondition and each Coenzyme Q10 treatment were collected at varioustimes following treatment, including after 24 hours and 48 hours oftreatment.

In addition, cross talk experiments between two different cancer cells,PaCa2 and HepG2 cells, were carried out in which PaCa2 and HepG2 cellswere co-cultured. This co-culturing approach is referred to as anextracellular secretome (ECS) experiment. The first cell system (PaCa2)was first seeded in the inserts of the wells of a transwell type growthchamber. Six well plates were used to enable better statisticalanalysis. At the time of seeding with the first cell system in theinserts, the inserts were placed in a separate 6-well plate. The secondcell system (HepG2) was seeded on the primary tray. The insert traycontaining the first cell system and the primary tray containing thesecond cell system were incubated at 37° C. overnight. Each of the cellsystems was grown in the specific cell specific media (whereinalternatively, each of the cell systems could be grown in a mediumadapted to support the growth of both cell types). On the second day,the pre-determined treatment was given by media exchange. Specifically,the inserts containing the first cell system were placed into theprimary tray containing the second cell system. The tray was thenincubated for a pre-determined time period, e.g., 24 hour or 48 hours.Duplicate wells were set up with the same conditions, and cells werepooled to yield sufficient material for 2D analysis. The media (1 mlaliquot), the cells from the inserts and the cells from the wells of theprimary tray were harvested as separate samples. The experiments wereconducted in triplicate in order to provide better statistical analysispower.

Cross-talk experiments were also conducted by “media swap” experiments.Specifically, a cultured media or “secretome” from the first cell system(PaCa2) was collected after 24 hrs or 48 hrs following perturbation orconditioning as described above and then added to the second cell system(HepG2) for 24-48 hrs. The final cultured media or “secretome” from thesecond cell system was then collected. All final secretomes weresubjected to proteomic analysis.

iProfiling of changes in total cellular protein expression byquantitative proteomics was performed for cell and media samplescollected for each cell line at each condition and with each“environmental perturbation”, i.e, Coenzyme Q10 treatment, using thetechniques described above in the detailed description. iProfiling ofchanges in total cellular protein expression by quantitative proteomicswas similarly performed for cell and media samples collected for eachco-cultured cell line at each condition with each treatment.

Further, bioenergetics profiling of the cancer, normal cells and cellsin cross-talk experiments exposed to each condition and with or withoutCoenzyme Q10 perturbation were generated by employing the Seahorseanalyzer essentially as recommended by the manufacturer. OCR (Oxygenconsumption rate) and ECAR (Extracullular Acidification Rate) wererecorded by the electrodes in a 7 μl chamber created with the cartridgepushing against the seahorse culture plate.

Proteomics data collected for each cell line (including cells incross-talk experiments) at each condition and with each perturbation,and bioenergetics profiling data collected for each cell line at eachcondition and with each perturbation, were all inputted and processed bythe REFS™ system. Raw data for Paca2, HepG2, THLE2 and cross-talkexperiments were then combined using a standardized nomenclature. Geneswith more than 15% of the proteomics data missing were filtered out.Data imputation strategy was developed. For example, a within replicateserror model was used to impute data from experimental conditions withreplicates. A K-NN algorithm based on 10 neighbors was used to imputedata with no replicates. Different REFS™ models were built for threebiological systems together, for just the Paca2 system, or for just theHepG2 system linked to the phenotypic data.

The area under the curve and fold changes for each edge connecting aparent node to a child node in the simulation networks were extracted bya custom-built program using the R programming language, where the Rprogramming language is an open source software environment forstatistical computing and graphics.

Output from the R program were inputted into Cytoscape, an open sourceprogram, to generate a visual representation of the consensus network.

Among all the models built, an exemplary protein interaction REFSconsensus network at 70% fragment frequency is shown in FIG. 21.

Each node in the consensus network shown in FIG. 21 was simulated byincreasing or decreasing expression of LDHA by 4-fold to generate asimulation network using REFS™, as described in detail above in thedetailed description.

The effect of simulated LDHA expression change on PARK7 and proteins innotes associated with PARK7 at high level in the exemplary consensusnetwork shown in FIG. 21 were investigated. Proteins responsive to theLDHA simulation in two cancer cell lines, i.e., Paca2 and HepG2, wereidentified using REFS™ (see FIG. 22). The numbers represent particularprotein expression level fold changes.

To validate the protein connections identified using the above method,markers identified to be in immediate proximity to LDHA in thesimulation network were inputted to IPA, a software program thatutilizes neural networks to determine molecular linkage betweenexperimental outputs to networks based on previously publishedliterature. Output of the IPA program is shown in FIG. 23, wherein themarkers in grey shapes were identified to be in immediate proximity toLDHA in the simulation network generated by the platform and the markersin unfilled shapes are connections identified by IPA based on knownknowledge in previously published literature.

Markers identified in the output from the Interrogative Biology platformtechnology (shown in FIG. 21), i.e. DHX9, HNRNPC, CKAP4, HSPA9, PARP1,HADHA, PHB2, ATP5A1 and CANX were observed to be connected to well-knowncancer markers such as TP53 and PARK7 within the IPA generated network(shown in FIG. 23). The fact that the factors identified by the use ofthe Interrogative Biology platform share connectivity with known factorspublished in the scientific literatures validated the accuracy of thenetwork created by the use of the Interrogative Biology Platform. Inaddition, the network association within the LDHA sub-network created bythe use of the Interrogative Biology platform outputs demonstrated thepresence of directional influence of each factor, in contrast to the IPAnetwork wherein the linkage between molecular entities does not providefunctional directionality between the interacting nodes. Thus, byemploying an unbiased approach to data generation, integration andreverse engineering to create a computational model followed bysimulation and differential network analysis, the Interrogative Biologydiscovery platform enables the understanding of hitherto unknownmechanisms in cancer pathophysiology that are in congruence withwell-established scientific understandings of disease pathophysiology.

FIG. 19 shows effect of CoQ10 treatment on downstream nodes (pubmedprotein accession numbers are listed in FIG. 19) based on the proteinexpression data from iProfiling. Protein accession number P00338 isLDHA. Wet lab validation of proteomics data were performed for LDHAexpression in HepG2 cells (see FIG. 20). As shown in FIG. 20, LDHAexpression levels were decreased when HepG2 were treated with 50 uMCoQ10 or 100 uM CoQ10 for 24 or 48 hours.

For the well know cancer markers TP53, Bcl-2, Bax and Caspase3, wet labvalidation of effects of CoQ10 treatment on these markers' expressionlevel in SKMEL 28 cells were performed (see FIG. 24 and FIG. 25).

Example 2: Employing Platform Technology to Build a Cancer Delta-DeltaNetwork

In this example, the platform technology described in detail above wasemployed to integrate data obtained from a custom built in vitro cancermodel, and thereby identity novel proteins/pathways driving thepathogenesis of cancer. Relational maps resulting from this analysishave provided cancer treatment targets, as well as diagnostic/prognosticmarkers associated with cancer.

Briefly, four cancer lines (PaCa2, HepG2, PC3 and MCF7) and two normalcells lines (THLE2 and HDFa) were subject to various conditionssimulating an environment experienced by cancer cells in vivo.Specifically, cells were exposed separately to each of hyperglycemicconditions, hypoxic conditions and treatment with lactic acid. Forexample, a hyperglycemic condition was created by culturing the cells inmedia containing 22 mM glucose. A hypoxic condition was induced byplacing the cells in a Modular Incubator Chamber (MIC-101,Billups-Rothenberg Inc. Del Mar, Calif.), which was flooded with anindustrial gas mix containing 5% CO₂, 2% O₂ and 93% nitrogen. For lacticacid treatment, each cell line was treated with 0 or 12.5 mM lacticacid. In addition to exposing the cells to each of the three foregoingconditions separately, cells were also exposed to combinations of two orall three of the conditions (i.e., hyperglycemic and hypoxic conditions;hyperglycemic condition and lactic acid; hypoxic condition and lacticacid; and, hyperglycemic and hypoxic conditions and lactic acid).

The cell model comprising the above-mentioned cells, wherein each typeof cell was exposed to each condition described above, was additionallyinterrogated by exposing the cells to an environmental perturbation bytreating with Coenzyme Q10. Specifically, the cells were treated withCoenzyme Q10 at 0, 50 μM or 100 μM.

Cell samples, as well as media samples containing the secretome from thecells, for each cell line exposed to each condition (or combination ofconditions), with and without Coenzyme Q10 treatment, were collected atvarious times following treatment, including after 24 hours and 48 hoursof treatment.

In addition, cross talk experiments between two different cancer cells,PaCa2 and HepG2 cells, were carried out in which PaCa2 and HepG2 cellswere co-cultured. This co-culturing approach is referred to as anextracellular secretome (ECS) experiment. The first cell system (PaCa2)was seeded in the inserts of the wells of a transwell type growthchamber. Six well plates were generally used in order to enable betterstatistical analysis. At the time of seeding of the first cell system inthe inserts, the inserts were placed in a separate 6-well plate. Thesecond cell system (HepG2) was seeded in the primary tray. The 6-wellplate containing the inserts, which contained the first cell system, andthe primary tray containing the second cell system were incubated at 37°C. overnight. Each of the cell systems was grown in its respective cellspecific media (wherein alternatively, each of the cell systems could begrown in a medium adapted to support the growth of both cell types). Onthe second day, the pre-determined treatment was given by mediaexchange. Specifically, the inserts containing the first cell system andthe first cell system's respective media were placed into the primarytray containing the second cell system and the second cell system'srespective media. In all cases of co-culture, however, co-cultured cellshad been exposed to the same “cancer condition” (e.g., hyperglycemia,hypoxia, lactic acid, or combinations thereof), albeit separately,during the first day prior to co-culturing. That is, the first cellsystem in the inserts and the second cell system in the trays wereexposed to the same condition before being moved to a “coculture”arrangement. The tray was then incubated for a pre-determined timeperiod, e.g., 24 hour or 48 hours. Duplicate wells were set up with thesame conditions, and cells were pooled to yield sufficient material forsubsequent proteomic analysis. The media containing the secretome (1 mlaliquot), the cells from the inserts and the cells from the wells of theprimary tray were harvested as separate samples. The experiments wereconducted in triplicate in order to provide better statistical power.

Cross-talk experiments were also conducted by “media swap” experiments.Specifically, a cultured media or “secretome” from the first cell system(PaCa2) was collected after 24 hrs or 48 hrs following perturbationand/or conditioning and then added to the second cell system for 24-48hrs. The final cultured media or “secretome” from the second cell systemwas then collected. All final secretomes were subjected to proteomicanalysis.

Following the exposure of the cell system to the “cancer conditions”described above, the perturbation (i.e., Coenzyme Q10 treatment), and/orthe conditions produced in the secretome of a paired cell from aco-culture experiment, the response of the cells was then analyzed byanalysis of various readouts from the cell system. The readouts includedproteomic data, specifically intracellular protein expression as well asproteins secreted into cell culture media, and functional data,specifically cellular bioenergetics.

iProfiling of changes in total cellular protein expression byquantitative proteomics was performed for cell and media samplescollected for each cell line (normal and cancer cell lines) exposed toeach condition (or combination of conditions), with or without the“environmental perturbation”, i.e., Coenzyme Q10 treatment, using thetechniques described above in the detailed description.

Further, bioenergetics profiling of each cell line (normal and cancercell lines) exposed to each condition (or combination of conditions),with or without the “environmental perturbation”, i.e., Coenzyme Q10treatment, were generated by employing the Seahorse analyzer essentiallyas recommended by the manufacturer. Oxygen consumption rate (OCR) andExtracullular Acidification Rate (ECAR) were recorded by the electrodesin a 7 μl chamber created with the cartridge pushing against theseahorse culture plate.

Proteomics data collected for each cell line at each condition(s) andwith/without each perturbation, and bioenergetics profiling datacollected for each cell line at each condition(s) and with/without eachperturbation, were then processed by the REFS™ system. A “compositecancer perturbed network” was generated from combined data obtained fromall of the cancer cell lines, each having been exposed to each specificcondition (and combination of conditions), and further exposed toperturbation (CoQ10). A “composite cancer unperturbed network” wasgenerated from combined data obtained from all of the cancer cell lines,each having been exposed to each specific condition (and combination ofconditions), without perturbation (without CoQ10). Similarly, a“composite normal perturbed network” was generated from combined dataobtained from all of the normal cell lines, each having been exposed toeach specific condition (and combination of conditions), andadditionally exposed to perturbation (CoQ10). A “composite normalunperturbed network” was generated from combined data obtained from allof the normal cell lines, each having been exposed to each specificcondition (and combination of conditions), without perturbation (withoutCoQ10).

Next, “simulation composite networks” (also referred to herein as“simulation networks”) were generated for each of the four compositenetworks described above using REFS™. To accomplish this, each node inthe given consensus composite network was simulated (by increasing ordecreasing by 10-fold) to generate simulation networks using REFS™, asdescribed in detail above in the detailed description.

The area under the curve and fold changes for each edge connecting aparent node to a child node in the simulation networks were extracted bya custom-built program using the R programming language, where the Rprogramming language is an open source software environment forstatistical computing and graphics.

Finally, delta networks were generated, where the delta networksrepresent the differential between two simulation composite networks.The delta networks were generated from the simulation compositenetworks. To generate a cancer vs. normal differential network inresponse to Coenzyme Q10 (delta-delta network), consecutive comparisonsteps were performed as illustrated in FIG. 26, by a custom builtprogram using the PERL programming language.

First, cancer untreated (T0) and cancer treated (T1) networks werecompared using the R program, and the unique Cancer treated T1 networkswere separated (see the crescent shape in dark grey in FIG. 26). Thisrepresents the Cancer T1∩(intersection) Cancer T0 “delta” network.Protein interaction/associations within this delta network can be viewedas representing the unique cancer response to Coenzyme Q10 treatment.

Similarly, normal untreated (T0) and normal treated (T1) networks werecompared using the R program, and the unique normal treated T1 networkswere separated (see the crescent shape in light grey in FIG. 26). Thisrepresents the Normal T1 ∩Normal T0 “delta” network. Proteininteractions/associations within this delta network can be viewed asrepresenting the unique normal cell response to Coenzyme Q10 treatment.

Finally, unique Cancer T1 networks (see the crescent shape in dark greyin FIG. 26) and unique normal T1 networks (see the crescent shape inlight grey in FIG. 26) were compared using the R program, and networksthat are unique to cancer alone, and not present in normal cells, inresponse to Coenzyme Q10 were generated (see FIG. 26). This collectionof protein interactions/associations represents the unique pathwayswithin cancer cells that are not present in normal cells upon CoenzymeQ10 treatment. This collection of protein interactions/associations iscalled a “delta-delta network,” since it is a differential map producedfrom a comparison of a differential map from cancer cells and adifferential map from normal control cells.

Output from the PERL and R programs were input into Cytoscape, an opensource program, to generate a visual representation of the Delta-Deltanetwork.

The delta-delta networks identified using the method described hereinare highly useful for identifying targets for cancer treatment. Forexample, according to the delta-delta network presented in FIG. 27,Protein A inhibits OCR3 (a measurement for oxydative phosphorylation)and enhances ECAR3 (a measurement for glycolysis). Since thisinteraction is unique in cancer cells (because the delta-delta networkhas subtracted any interactions that are commonly present in normalcells upon Coenzyme Q10 treatment), inhibiting the expression of proteinA is expected to reduce glycolysis-based energy metabolism, which is ahallmark of the cancer metabolic pathway, and shift the cells towards anoxidative phosphorylation-based energy metabolism, which is a phenotypemore closely associated with normal cells. Thus, a combination therapyusing Coenzyme Q10 and protein A inhibitor is expected to be effectiveto treat cancer, at least in part by shifting the energy metabolismprofile of the cancer cell to that which resembles a normal cell.

The advantage of the Interrogative Biology platform technology of theinvention is further illustrated by the use of a substantive examplewherein a sub-network derived from causal networks was compared tomolecular network using IPA, a software program that utilizes neuralnetworks to determine molecular linkage between experimental outputs tonetworks based on previously published literature. The causalsub-network containing PARK7 generated using the Interrogative Biologyplatform (shown in FIG. 29) is used as a substantive example. Allmolecular signatures of the PARK7 network from the Interrogative Biologyplatform were incorporated into IPA to generate a network based onknown/existing literature evidence. The network outputs between theInterrogative Biology output and that generated by the use of IPA wasthen compared.

Six markers identified by the output from the Interrogative Biologyplatform technology (shown in FIG. 29), i.e. A, B, C, X, Y and Z inFIGS. 27-29, were observed to be connected to TP53 within the IPAgenerated network (FIG. 28). Among the six markers, A, B and C have beenreported in the literature to be associated with cancer, as well asHSPA1A/HSPA1B. X, Y and Z were identified as “hubs” or key drivers ofthe cancer state, and are therefore identified as novel cancer markers.Further, MIF1 and KARS were also identified as “hubs” or key drivers ofthe cancer state, and are therefore identified as novel cancer markers.The fact that the factors identified by the use of the InterrogativeBiology platform share connectivity with known factors published in thescientific literatures validated the accuracy of the network created bythe use of the Interrogative Biology Platform. In addition, the networkassociation within the PARK7 sub-network created by the use of theInterrogative Biology platform outputs (shown in FIG. 29) demonstratedthe presence of directional influence of each factor, in contrast to theIPA network (shown in FIG. 28) wherein the linkage between molecularentities does not provide functional directionality between theinteracting nodes. Furthermore, outputs from the Interrogative Biologyplatform (shown as dotted lines in FIG. 29) demonstrated the associationof these components leading to a potential mechanism through PARK7.Protein C, Protein A and other nodes of PARK7 were observed to be keydrivers of cancer metabolism (FIG. 27).

As evidenced by the present example, by employing an unbiased approachto data generation, integration and reverse engineering to create acomputational model followed by simulation and differential networkanalysis, the Interrogative Biology discovery platform enables theunderstanding of hitherto unknown mechanisms in cancer pathophysiologythat are in congruence with well-established scientific understandingsof disease pathophysiology.

Example 3: Employing Platform Technology to Build aDiabetes/Obesity/Cardiovascular Disease Delta-Delta Network

In this example, the platform technology described in detail above inthe detailed description was employed to integrate data obtained from acustom built diabetes/obesity/cardiovascular disease (CVD) model, and toidentity novel proteins/pathways driving the pathogenesis ofdiabetes/obesity/CVD. Relational maps resulting from this analysis haveprovided diabetes/obesity/CVD treatment targets, as well asdiagnostic/prognostic markers associated with diabetes/obesity/CVD.

Five primary human cell lines, namely adipocytes, myotubes, hepatocytes,aortic smooth muscle cells (HASMC), and proximal tubular cells (HK2)were subject to one of five conditions simulating an environmentexperienced by these disease-relevant cells in vivo. Specifically, eachof the five cell lines were exposed separately to each of the followingconditions: hyperglycemic conditions, hyperlipidemic conditions,hyperinsulinemic conditions, hypoxic conditions and exposure to lacticacid. The hyperglycemic condition was induced by culturing cells inmedia containing 22 mM glucose. The hyperlipidemic condition was inducedby culturing the cells in media containing 0.15 mM sodium palmitate. Thehyperinsulinemic condition was induced by culturing the cells in mediacontaining 1000 nM insulin. The hypoxic condition was induced by placingthe cells in a Modular Incubator Chamber (MIC-101, Billups-RothenbergInc. Del Mar, Calif.), which was flooded with an industrial gas mixcontaining 5% CO₂, 2% O₂ and 93% nitrogen. Each cell line was alsotreated with 0 or 12.5 mM lactic acid.

In addition, cross talk experiments between two different pairs ofcells, HASMC (cell system 1) and HK2 cells (cell system 2) or livercells (cell system 1) and adipocytes (cellsystem 2) were carried out inwhich the paired cells were co-cultured. This co-culturing approach isreferred to as an extracellular secretome (ECS) experiment. The firstcell system (e.g., HASMC) was first seeded in the inserts of the wellsof a transwell type growth chamber. Six well plates were used to enablebetter statistical analysis. At the time of seeding with the first cellsystem in the inserts, the inserts were placed in a separate 6-wellplate. The second cell system (e.g., HK2) was seeded on the primarytray. The insert tray containing the first cell system and the primarytray containing the second cell system were incubated at 37° C.overnight. Each of the cell systems was grown in the specific cellspecific media (wherein alternatively, each of the cell systems could begrown in a medium adapted to support the growth of both cell types). Onthe second day, the pre-determined treatment was given by mediaexchange. Specifically, the inserts containing the first cell systemwere placed into the primary tray containing the second cell system. Thetray was then incubated for a pre-determined time period, e.g., 24 houror 48 hours. Duplicate wells were set up with the same conditions, andcells were pooled to yield sufficient material for 2D analysis. Themedia (1 ml aliquot), the cells from the inserts and the cells from thewells of the primary tray were harvested as separate samples. Theexperiments were conducted in triplicate in order to provide betterstatistical analysis power.

Cross-talk experiments were also conducted by “media swap” experiments.Specifically, a cultured media or “secretome” from the first cellsystem, HASMC was collected after 24 hrs or 48 hrs followingperturbation or conditioning and then added to the second cell system,Adipoctes, for 24-48 hrs. The final cultured media or “secretome” fromthe second cell system was then collected. All final secretomes weresubjected to proteomic analysis.

The cell model comprising the above-mentioned cells, wherein the cellswere exposed to each condition described above, was additionally“interrogated” by exposing the cells to an “environmental perturbation”by treating with Coenzyme Q10. Specifically, the cells were treated withCoenzyme Q10 at 0, 50 μM, or 100 μM.

Cell samples for each cell line, condition and Coenzyme Q10 treatmentwere collected at various times following treatment, including after 24hours and 48 hours of treatment. For certain cells and under certainconditions, media samples were also collected and analyzed.

iProfiling of changes in total cellular protein expression byquantitative proteomics was performed for cell and media samplescollected for each cell line at each condition and with each“environmental perturbation”, i.e, Coenzyme Q10 treatment, using thetechniques described above in the detailed description.

Proteomics data collected for each cell line listed above at eachcondition and with each perturbation, and bioenergetics profiling datacollected for each cell line at each condition and with eachperturbation, were then processed by the REFS™ system. A compositeperturbed network was generated from combined data obtained from all thecell lines for one specific condition (e.g., hyperglycemia) exposed toperturbation (CoQ10). A composite unperturbed network was generated fromcombined data obtained from all of the cell lines for the same onespecific condition (e.g., hyperglycemia), without perturbation (withoutCoQ10). Similarly, a composite perturbed network was generated fromcombined data obtained from all of the cell lines for a second, controlcondition (e.g., normal glycemia) exposed to perturbation (CoQ10). Acomposite unperturbed network was generated from combined data obtainedfrom all of the cell lines for the same second, control condition (e.g.,normal glycemia), without perturbation (without CoQ10).

Each node in the consensus composite networks described above wassimulated (by increasing or decreasing by 10-fold) to generatesimulation networks using REFS™, as described in detail above in thedetailed description.

The area under the curve and fold changes for each edge connecting aparent node to a child node in the simulation networks were extracted bya custom-built program using the R programming language, where the Rprogramming language is an open source software environment forstatistical computing and graphics.

Delta networks were generated from the simulated composite networks. Togenerate a Diabetes/Obesity/Cardiovascular disease condition vs. normalcondition differential network in response to Coenzyme Q10 (delta-deltanetwork), steps of comparison were performed as illustrated in FIG. 30,by a custom built program using the PERL programming language.

Specifically, as shown in FIG. 30, Treatment T1 refers to Coenzyme Q10treatment and NG and HG refer to normal and hyperglycemia as conditions.Unique edges from NG in the NG∩HG delta network was compared with uniqueedges of HGT1 in the HG∩HGT1 delta network. Edges in the intersection ofNG and HGT1 are HG edges that are restored to NG with T1. HG edgesrestored to NG with T1 were superimposed on the NG∩HG delta network(shown in darker colored circles in FIG. 31)

Specifically, a simulated composite map of normal glycemia (NG)condition and a simulated composite map of hyperglycemia (HG) conditionwere compared using a custom-made Perl program to generate unique edgesof the normal glycemia condition. A simulated composite map ofhyperglycemia condition without Coenzyme Q10 treatment (HG) and asimulated map of hyperglycemia condition with Coenzyme Q10 treatment(HGT1) were compared using a custom-made Perl program to generate uniqueedges of the hyperglycemia condition with Coenzyme Q10 treatment (HGT1).Edges in the intersection of the unique edges from normal glycemiacondition (NG) and the unique edges from hyperglycemia condition withCoenzyme Q10 treatment (HGT1) were identified using the Perl program.These edges represent factors/networks that are restored to normalglycemia condition from hyperglycemia condition by the treatment ofCoenzyme Q10. The delta-delta network of hyperglycemic edges restored tonormal with Coenzyme Q10 treatment was superimposed on the normalglycemia ∩ Hyperglycemia delta network. A sample of the superimposednetworks is shown in FIG. 31. FIG. 31 is an exemplarydiabetes/obesity/cardiovascular disease condition vs. normal conditiondifferential network in response to Coenzyme Q10 (delta-delta network).Darker colored circles in FIG. 31 are identified edges which wererestored to a normal glycemia condition from a hyperglycemia conditionby the treatment of Coenzyme Q10. Lighter colored circles in FIG. 31 areidentified unique normal hypercemia edges.

Output from the PERL and R programs were input into Cytoscape, an opensource program, to generate a visual representation of the Delta-Deltanetwork.

Similarly to the experiments described above for hyperglycemia vs.normal glycemic condition, a simulated composite network ofhyperlipidemia condition (combining data from alldiabetes/obesity/cardiovascular-related cells described above) withoutCoenzyme Q10 treatment and a simulated composite network ofhyperlipidemia condition (combining data from alldiabetes/obesity/cardiovascular-related cells, described above) withCoenzyme Q10 treatment were compared using the Perl program to generateunique edges of the hyperlipidemia condition with Coenzyme Q10treatment. Edges in the intersection of the unique edges from normallipidemia condition and the unique edges from hyperlipidemic conditionwith Coenzyme Q10 treatment were identified using the Perl program.These edges represent factors/networks that are restored to a normallipidemia condition from a hyperlipidemia condition by the treatment ofCoenzyme Q10. A delta-delta network of hyperlipidemic edges restored tonormal with Coenzyme Q10 treatment was superimposed on the normallipidemia ∩ Hyperlipidemia delta network. A sample of the superimposednetworks is shown in FIG. 32. Darker colored circles in FIG. 32 areidentified edges which were restored to a normal lipidemia conditionfrom a hyperlipidemia condition by the treatment of Coenzyme Q10.Lighter colored circles in FIG. 32 are identified unique normallipidemia edges. FASN was identified as one important factor of asignaling pathway which modulates Coenzyme Q10's effect of restoringhyperlipidemia to a normal lipidemia condition.

Fatty acid synthase-fatty acid synthesis enzymes such as FASN have beenimplicated in almost all aspects of human metabolic alterations such asobesity, insulin resistance or dyslipidemia. FASN inhibitors have beenproposed as lead molecules for treatment of obesity, although molecularmechanisms are unknown (Mobbs et al 2002). Cerulenin and syntheticcompound C75—FASN inhibitors have been shown to have an effect inreducing food intake and effectuate weight loss (Loftus et al 2000).

The fact that FASN was identified by the platform technology describedherein as one important factor in the signaling pathway which modulatesCoenzyme Q10's effect of restoring a diabetic to a normal state, asshown in FIG. 32, validated the accuracy of this delta-delta network.Therefore, other novel-factors identified in this delta-delta networkwill be potential therapeutic factors or drug targets for furtherinvestigation.

Example 4: Employing Platform Technology to Build Models of Drug InducedCardiotoxicity

In this example, the platform technology described in detail above inthe detailed description was employed to integrate data obtained from acustom built cardiotoxicity model, and to identify novelproteins/pathways driving the pathogenesis/toxicity of drugs. Relationalmaps resulting from this analysis have provided toxicity biomarkers.

In the healthy heart contractile function depends on a balance of fattyacid and carbohydrate oxidation. Chronic imbalance in uptake,utilization, organellar biogenesis and secretion in non-adipose tissue(heart and liver) is thought to be at the center of mitochondrial damageand dysfunction and a key player in drug induced cardiotoxicity. HereApplicants describe a systems approach combining protein and lipidsignatures with functional end point assays specifically looking atcellular bioenergetics and mitochondrial membrane function. In vitromodels comprising diabetic and normal cardiomyocytes supplemented withexcessive fatty acid and hyperglycemia were treated with a panel ofdrugs to create signatures and potential mechanisms of toxicity.Applicants demonstrated the varied effects of drugs in destabilizing themitochondria by disrupting the energy metabolism component at variouslevels including (i) Dysregulation of transcriptional networks thatcontrols expression of mitochondrial energy metabolism genes; (ii)Induction of GPAT1 and taffazin in diabetic cardiomyocytes therebyinitiating de novo phospholipid synthesis and remodeling in themitochondrial membrane; and (iii) Altered fate of fatty acid in diabeticcardiomyocytes, influencing uptake, fatty acid oxidation and ATPsynthesis. Further, Applicants combined the power of wet lab biology andAI based data mining platform to generate causal network based onbayesian models. Networks of proteins and lipids that are causal forloss of normal cell function were used to discern mechanisms of druginduced toxicity from cellular protective mechanisms. This novelapproach will serve as a powerful new tool to understand mechanism oftoxicity while allowing for development of safer therapeutics thatcorrect an altered phenotype.

Human cardiomyocytes were subject to conditions simulating an diabeticenvironment experienced by the disease-relevant cells in vivo.Specifically, the cells were exposed to hyperglycemic conditions andhyperlipidemia conditions. The hyperglycemic condition was induced byculturing cells in media containing 22 mM glucose. The hyperlipidemiacondition was induced by culturing the cells in media containing 1 mML-carnitine, 0.7 mM Oleic acid and 0.7 mM Linoleic acid.

The cell model comprising the above-mentioned cells, wherein the cellswere exposed to each condition described above, was additionally“interrogated” by exposing the cells to an “environmental perturbation”by treating with a diabetic drug (T) which is known to causecardiotoxicity, a rescue molecule (R) or both the diabetic drug and therescue molecule (T+R). Specifically, the cells were treated withdiabetic drug; or treated with rescue molecule Coenzyme Q10 at 0, 50 μM,or 100 μM; or treated with both of the diabetic drug and the rescuemolecule Coenzyme Q10.

Cell samples from each condition with each perturbation treatment werecollected at various times following treatment, including after 6 hoursof treatment. For certain conditions, media samples were also collectedand analyzed.

iProfiling of changes in total cellular protein expression byquantitative proteomics was performed for cell and media samplescollected for each condition and with each “environmental perturbation”,i.e, diabetic drug treatment, Coenzyme Q10 treatment or both, using thetechniques described above in the detailed description. Transcriptionalprofiling experiments were carried out using the Biorad cfx-384amplification system. Following data collection (Ct), the final foldchange over control was determined using the δCt method as outlined inmanufacturer's protocol. Lipidomics experiments were carried out usingmass spectrometry. Functional assays such as Oxygen consumption rate OCRwere measured by employing the Seahorse analyzer essentially asrecommended by the manufacturer. OCR was recorded by the electrodes in a7 μl chamber created with the cartridge pushing against the seahorseculture plate.

As shown in FIG. 35, transcriptional network and expression of humanmitochondrial energy metabolism genes in diabetic cardiomyocytes(cardiomyocytes conditioned in hyperglycemic and hyperlipidemia) werecompared between perturbed and unperturbed treatments. Specifically,data of transcriptional network and expression of human mitochondrialenergy metabolism genes were compared between diabetic cardiomyocytestreated with diabetic drug (T) and untreated diabetic cardiomyocytessamples (UT). Data of Transcriptional network and expression of humanmitochondrial energy metabolism genes were compared between diabeticcardiomyocytes treated with both diabetic drug and rescue moleculeCoenzyme Q10 (T+R) and untreated diabetic cardiomyocytes samples (UT).Comparing to data from untreated diabetic cardiomyocytes, certain genesexpression and transcription were altered when diabetic cardiomyocyteswere treated with diabetic drug. Rescue molecule Coenzyme Q10 wasdemonstrated to reverse the toxic effect of diabetic drug and normalizegene expression and transcription.

As shown in FIG. 36A, cardiomyocytes were cultured either innormoglycemia (NG) or hyperglygemia (HG) condition and treated witheither diabetic drug alone (T) or with both diabetic drug and rescuemolecule Coenzyme Q10 (T+R). Protein expression levels of GPAT1 and TAZfor each condition and each treatment were tested with western blotting.Both GPAT1 and TAZ were upregulated in hyperglycemia conditioned anddiabetic drug treated cardiomyocytes. When hyperglycemia conditionedcardiomyocytes were treated with both diabetic drug and rescue moleculeCoenzyme Q10, the upregulated protein expression level of GPAT1 and TAZwere normalized.

As shown in FIG. 37A, mitochondrial oxygen consumption rate (%)experiments were carried out for hyperglycemia conditionedcardiomyocytes samples. Hyperglycemia conditioned cardiomyocytes wereeither untreated (UT), treated with diabetic drug T1 which is known tocause cardiotoxicity, treated with diabetic drug T2 which is known tocause cardiotoxicity, treated with both diabetic drug T1 and rescuemolecule Coenzyme Q10 (T1+R), or treated with both diabetic drug T2 andrescue molecule Coenzyme Q10 (T2+R). Comparing to untreated controlsamples, mitochondrial OCR was decreased when hyperglycemia conditionedcardiomyocytes were treated with diabetic drug T1 or T2. However,mitochondrial OCR was normalized when hyperglycemia conditionedcardiomyocytes were treated with both diabetic drug and rescue moleculeCoenzyme Q10 (T1+R, or T2+R).

As shown in FIG. 37B, mitochondria ATP synthesis experiments werecarried out for hyperglycemia conditioned cardiomyocytes samples.Hyperglycemia conditioned cardiomyocytes were either untreated (UT),treated with a diabetic drug (T), or treated with both diabetic drug andrescue molecule Coenzyme Q10 (T+R). Comparing to untreated controlsamples, mitochondrial ATP synthesis was repressed when hyperglycemiaconditioned cardiomyocytes were treated with diabetic drug (T).

As shown in FIG. 38, based on the collected proteomic data, proteinsdown regulated by drug treatment were annotated with GO terms. Proteinsinvolved in mitochondrial energy metabolism were down regulated whenhyperglycemia conditioned cardiomyocytes were treated with a diabeticdrug which is known to cause cardiotoxicity.

Proteomics, lipidomics, transcriptional profiling, functional assays,and western blotting data collected for each condition and with eachperturbation, were then processed by the REFS™ system. Compositeperturbed networks were generated from combined data obtained from onespecific condition (e.g., hyperglycemia, or hyperlipidemia) exposed toeach perturbation (e.g., diabetic drug, CoQ10, or both). Compositeunperturbed networks were generated from combined data obtained from thesame one specific condition (e.g., hyperglycemia, or hyperlipidemia),without perturbation (untreated). Similarly, composite perturbednetworks were generated from combined data obtained for a second,control condition (e.g., normal glycemia) exposed to each perturbation(e.g., diabetic drug, CoQ10, or both). Composite unperturbed networkswere generated from combined data obtained from the same second, controlcondition (e.g., normal glycemia), without perturbation (untreated).

Each node in the consensus composite networks described above wassimulated (by increasing or decreasing by 10-fold) to generatesimulation networks using REFS™, as described in detail above in thedetailed description.

The area under the curve and fold changes for each edge connecting aparent node to a child node in the simulation networks were extracted bya custom-built program using the R programming language, where the Rprogramming language is an open source software environment forstatistical computing and graphics.

Delta networks were generated from the simulated composite networks. Togenerate a drug induced toxicity condition vs. normal conditiondifferential network in response to the diabetic drug (delt network),steps of comparison were performed as illustrated in FIG. 39, by acustom built program using the PERL programming language.

Specifically, as shown in FIG. 39, UT refers to protein expressionnetworks of untreated control cardiomyocytes in hyperglycemia condition.Treatment T refers to protein expression networks of diabetic drugtreated cardiomyocytes in hyperglycemia condition. Unique edges from Tin the UT∩T delta network are presented in FIG. 40.

Specifically, a simulated composite map of untreated cardiomyocytes inhyperglycemia condition and a simulated composite map of diabetic drugtreated cardiomyocytes in hyperglycemia condition were compared using acustom-made Perl program to generate unique edges of the diabetic drugtreated cardiomyocytes in hyperglycemia condition. Output from the PERLand R programs were input into Cytoscape, an open source program, togenerate a visual representation of the delta network. As shown in FIG.40, the network represents delta networks that are driven by thediabetic drug versus untreated in cardiomyocytes/cardiotox models inhyperglycemia condition.

From the drug induced toxicity condition vs. normal conditiondifferential network shown in FIG. 40, proteins were identified whichdrive pathophysiology of drug induced cardiotoxicity, such as GRP78,GRP75, TIMP1, PTX3, HSP76, PDIA4, PDIA1, CA2D1. These proteins canfunction as biomarkers for identification of other cardiotoxicityinducing drugs. These proteins can also function as biomarkers foridentification of agents which can alleviate cardiotoxicity.

The experiments described in this Example demonstrate that perturbedmembrane biology and altered fate of free fatty acid in diabeticcardiomyocytes exposed to drug treatment represent the center piece ofdrug induced toxicity. Data integration and network biology have allowedfor an enhanced understanding of cardiotoxicity, and identification ofnovel biomarkers predictive for cardiotoxicity.

INCORPORATION BY REFERENCE

The contents of all cited references (including literature references,patents, patent applications, and websites) that maybe cited throughoutthis application are hereby expressly incorporated by reference in theirentirety, as are the references cited therein. The practice of thepresent invention will employ, unless otherwise indicated, conventionaltechniques of protein formulation, which are well known in the art.

EQUIVALENTS

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The foregoingembodiments are therefore to be considered in all respects illustrativerather than limiting of the invention described herein. Scope of theinvention is thus indicated by the appended claims rather than by theforegoing description, and all changes that come within the meaning andrange of equivalency of the claims are therefore intended to be embracedherein.

We claim:
 1. A method for identifying a modulator of a disease process, said method comprising: (1) obtaining a first data set representing measured expression levels of a plurality of genes in an in vitro culture of disease related cells; (2) obtaining a first control data set representing measured expression levels of a plurality of genes in an in vitro culture of control cells; (3) obtaining a second data set representing a measured functional activity or a measured cellular response of the in vitro culture of disease related cells; (4) obtaining a second control data set representing a measured functional activity or a measured cellular response of the in vitro culture of control cells; (5) generating a computer-implemented first causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein generating the computer-implemented first causal relationship network model comprises: (i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the expression levels of the plurality of genes and the functional activity or cellular response in the in vitro culture of the disease related cells; (ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and (iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors, wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network, and wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network; wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first data set and the second data set; (6) generating a computer-implemented second causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response of the control cells based solely on the first control data set and the second control data set using the programmed computing system, wherein generating the computer-implemented second causal relationship network model comprises: (i) creating a second list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first control data set and the second control data set, wherein the variables correspond to the expression levels of the plurality of genes and the functional activity or cellular response in the in vitro culture of control cells; (ii) creating a second ensemble of trial networks, each trial network constructed from a different subset of the second list of network fragments; and (iii) globally optimizing the second ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors, wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network, wherein evolving a trial network includes adding a network fragment from the second list to the trial network or replacing a network fragment in the trial network with a network fragment from the second list and determining whether the addition or replacement improves a total probabilistic score for the trial network; (7) generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device by steps including: i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and (8) identifying a causal relationship unique in the disease process from the generated differential causal relationship network, wherein a gene associated with the unique causal relationship is identified as a modulator of the disease process.
 2. The method of claim 1, wherein the disease process is cancer, diabetes, obesity or cardiovascular disease.
 3. The method of claim 2, wherein the cancer is lung cancer, breast cancer, prostate cancer, melanoma, squamous cell carcinoma, colorectal cancer, pancreatic cancer, thyroid cancer, endometrial cancer, bladder cancer, kidney cancer, a solid tumor, leukemia, non-Hodgkin lymphoma, or a drug-resistant cancer.
 4. The method of claim 1, wherein the modulator stimulates or promotes the disease process.
 5. The method of claim 1, wherein the modulator inhibits the disease process.
 6. The method of claim 5, wherein the modulator shifts an energy metabolic pathway specifically in disease cells from a glycolytic pathway towards an oxidative phosphorylation pathway.
 7. The method of claim 1, wherein the disease related cells in the in vitro culture of disease related cells were subjected to an environmental perturbation prior to measurements of expression levels for the first data set, and the control cells in the in vitro culture of control cells were not subjected to the environmental perturbation prior to measurements of expression levels for the first control data set.
 8. The method of claim 7, wherein the environmental perturbation comprised one or more of a contact with an agent, a change in culture condition, an introduced genetic modification or mutation, and a vehicle that caused a genetic modification or mutation.
 9. The method of claim 1, wherein the characteristic aspect of the disease process comprises a hypoxia condition, a hyperglycemic condition, a lactic acid rich culture condition, or combinations thereof.
 10. The method of claim 1, wherein the first data set and the first control data set comprise protein and/or mRNA expression levels of the plurality of genes.
 11. The method of claim 1, wherein the first data set and the first control data set further comprise one or more of lipidomics data, metabolomics data, transcriptomics data, and single nucleotide polymorphism (SNP) data.
 12. The method of claim 1, wherein the second data set and the second control data set comprise data obtained through one or more of bioenergetics profiling, a cell proliferation assay, an apoptosis assay, an organellar function assay, and a genotype-phenotype association assay actualized by functional models selected from ATP, ROS, OXPHOS, and Seahorse assays.
 13. The method of claim 1, wherein steps (5) and (6) are carried out by an artificial intelligence (AI)-based informatics platform that includes the programmed computing system.
 14. The method of claim 13, wherein the AI-based informatics platform receives all data input from the first data set, the first control data set, the second data set and the second control data set without applying a statistical cut-off point.
 15. The method of claim 1, wherein the generated first causal relationship network model is a first simulation causal relationship network model; wherein the optimized ensemble of trial networks based on the first data set and the second data set is a first consensus relationship network model; and wherein step (5) further comprises (iv) refining, by in silico simulation based on input data, the first consensus relationship network model to a first simulation causal relationship network model to provide a confidence level of prediction for one or more causal relationships within the first causal relationship network model.
 16. The method of claim 1, further comprising validating the identified unique causal relationship in a biological system.
 17. The method of claim 1, further comprising generating a delta-delta causal relationship network based on the first differential causal relationship network and a second differential causal relationship network generated solely based on data obtained from comparison cells.
 18. The method of claim 17, wherein the comparison cells are normal cells.
 19. The method of claim 1, further comprising displaying a graphical representation of the generated differential causal relationship network.
 20. The method of claim 1, further comprising storing a representation of the generating differential causal relationship network.
 21. The method of claim 1, wherein the at least one significantly different parameter is a directionality of the relationship or a quantitative magnitude of the strength of the relationship.
 22. The method of claim 1, wherein the method further comprises generating a graphical representation of the differential causal relationship network; and wherein the causal relationship unique in the disease process is identified from the graphical representation of the differential causal relationship network. 